Catalyst deterioration detection device, catalyst deterioration detection system, data analysis device, control device of internal combustion engine, and method for providing state information of used vehicle

ABSTRACT

A catalyst deterioration detection device is provided to detect deterioration of a catalyst provided in an exhaust passage of an internal combustion engine. The catalyst deterioration detection device includes a storage device and processing circuitry. The storage device stores map data specifying a mapping that uses time series data of an excess amount variable in a first predetermined period and time series data of a downstream detection variable in a second predetermined period as inputs to output a deterioration level variable. The processing circuitry executes an acquisition process that acquires data, a deterioration level variable calculation process that calculates a deterioration level variable of the catalyst based on an output of the mapping using the data acquired by the acquisition process as an input. The map data includes data that is learned through machine learning.

BACKGROUND 1. Field

The following description relates to a catalyst deterioration detectiondevice for a catalyst provided in an exhaust passage of an internalcombustion engine, a catalyst deterioration detection system, a dataanalysis device, a control device of an internal combustion engine, anda method for providing state information of a used vehicle.

2. Description of Related Art

Japanese Laid-Open Patent Publication No. 2012-117406 discloses anexample of a device that intentionally controls the air-fuel ratio of amixture that is burned in a combustion chamber to obtain a lean air-fuelratio so that the oxygen storage amount of a catalyst is maximized.After maximizing the oxygen storage amount of the catalyst, the deviceintentionally controls the air-fuel ratio of the mixture to obtain arich air-fuel ratio. When the air-fuel ratio of the mixture isintentionally controlled to the rich air-fuel ratio, the device obtainsa detection value of an air-fuel ratio sensor provided downstream of thecatalyst. The device detects that the oxygen storage amount of thecatalyst becomes zero based on the acquired detection value of theair-fuel ratio sensor to calculate the maximum value of the oxygenstorage amount of the catalyst. Since the maximum value of the oxygenstorage amount decreases due to the deterioration of the catalyst overtime, the maximum value indicates the deterioration level of thecatalyst.

In the device described above, in order to detect the deteriorationlevel of the catalyst, the air-fuel ratio of the mixture is deviatedfrom a value appropriate to exhaust purification control. This mayextend a period of time in which the air-fuel ratio of the mixture isdeviated from the appropriate value or may increase the amount ofdeviation of the air-fuel ratio of the mixture from the appropriatevalue. As a result, deviation of the composition amount of a fluidflowing into the catalyst from a composition amount that is appropriateto the purification performance of the catalyst may accumulate, and theaccumulated amount of deviation may increase.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Hereinafter, a plurality of aspects and effects of the presentdisclosure will be described.

Aspect 1. A catalyst deterioration detection device configured to detecta deterioration of a catalyst provided in an exhaust passage of aninternal combustion engine is provided. The catalyst deteriorationdetection device includes a storage device and processing circuitry. Thestorage device stores map data. The map data specifies a mapping thatuses time series data of an excess amount variable in a firstpredetermined period and time series data of a downstream detectionvariable in a second predetermined period as inputs to output adeterioration level variable. An amount of fuel that reacts with oxygencontained in a fluid flowing into the catalyst without excess ordeficiency is an ideal fuel amount. The excess amount variable is avariable that corresponds to an excess amount of an actual fuel flowinginto the catalyst in relation to the ideal fuel amount. The downstreamdetection variable is a variable that corresponds to a detection valueof an air-fuel ratio sensor provided downstream of the catalyst. Thedeterioration level variable is a variable related to a deteriorationlevel of the catalyst. The processing circuitry is configured to executean acquisition process that acquires the time series data of the excessamount variable in the first predetermined period and the time seriesdata of the downstream detection variable in the second predeterminedperiod, a deterioration level variable calculation process thatcalculates the deterioration level variable of the catalyst based on anoutput of the mapping using the data acquired by the acquisition processas an input, and a dealing process that operates a predeterminedhardware when the deterioration level of the catalyst is greater than orequal to a predetermined level based on a calculation result of thedeterioration level variable calculation process in response to asituation in which the deterioration level of the catalyst is greaterthan or equal to the predetermined level. The map data includes datathat is learned through machine learning.

In the configuration described above, the deterioration level variableis calculated by the mapping that uses the time series data of theexcess amount variable and the time series data of the downstreamdetection variable as inputs. The excess amount variable refers to anexcess amount of an actual fuel in relation to an amount of fuel thatreacts with oxygen without excess or deficiency. When the actual fuelamount is deficient, the excess amount variable has a negative value.Since the time series data of the excess amount variable allows forobtainment of information regarding oxygen and fuel flowing into thecatalyst, and the time series data of the downstream detection variableallows for obtainment of information regarding oxygen and unburned fuelflowing downstream of the catalyst, it is possible to obtain informationon the maximum value of the oxygen storage amount of the catalyst.Furthermore, it is possible to obtain information regarding thedeterioration level of the catalyst. In addition, since theabove-described time series data is used, the air-fuel ratio of themixture does not necessarily have to be deviated from a value that isappropriate for exhaust purification control. Even when the air-fuelratio of the mixture is deviated from the appropriate value, the periodof time in which the air-fuel ratio of the mixture is deviated from theproper value may be shortened, and the amount of deviation of theair-fuel ratio of the mixture from the appropriate value may bedecreased. This reduces an accumulated amount of deviation of thecomposition amount of the fluid flowing into the catalyst from thecomposition amount that is appropriate to the purification performanceof the catalyst. Moreover, in the configuration described above, the mapdata is learned through machine learning. Thus, the number of man-hoursfor associating the time series data of the excess amount variable andthe time series data of the downstream detection variable with thedeterioration level variable are reduced as compared to a case in whichthe adaptation is performed by humans.

Aspect 2. In the catalyst deterioration detection device according toaspect 1, the time series data in the second predetermined periodincludes values of the downstream detection variable that correspond tothree or more different points in time.

Aspect 3. In the catalyst deterioration detection device according toaspect 1 or 2, an input to the mapping includes a temperature of thecatalyst, the acquisition process includes a process that acquires thetemperature of the catalyst, and the deterioration level variablecalculation process includes a process that calculates the deteriorationlevel variable of the catalyst based on an output of the mapping thatuses the temperature of the catalyst as an input.

The maximum value of the oxygen storage amount of the catalyst changesin accordance with the temperature of the catalyst. In the configurationdescribed above, the input to the mapping includes the temperature ofthe catalyst. Thus, the deterioration level variable is calculated withhigh accuracy while determining whether the oxygen storage amount of thecatalyst is reduced due to the deterioration or due to the temperature.

Aspect 4. In the catalyst deterioration detection device according toany one of aspects 1 to 3, the excess amount variable includes avariable that corresponds to a detection value of an air-fuel ratiosensor provided upstream of the catalyst.

In the configuration described above, the excess amount variableincludes a variable related to a detection value of the upstreamair-fuel ratio sensor. Thus, a period of time that is taken for theexcess amount variable to affect a detection value of the downstreamair-fuel ratio sensor is shortened as compared to a case in which onlythe amounts of fuel and air that are provided for combustion in acombustion chamber are used as the excess amount variables. In otherwords, the configuration described above shortens a period of time fromwhen the value of the excess amount variable is acquired to when theeffect corresponding to the acquired value of the excess amount variableappears in the detection value of the downstream air-fuel ratio sensor.For this reason, the number of pieces of time series data is readilyreduced as compared to a case in which only the amounts of fuel and airthat are provided for combustion in the combustion chamber are used asthe excess amount variables.

Aspect 5. In the catalyst deterioration detection device according toany one of aspects 1 to 4, the map data is one of different kinds of mapdata, and the storage device includes the different kinds of map data,and the deterioration level variable calculation process includes aselection process that selects the map data from the different kinds ofmap data, the map data being used to calculate the deterioration levelvariable of the catalyst.

If a single mapping is configured to be capable of outputting thedeterioration level variable in various situations with high accuracy,the structure of the mapping becomes more complicated. In this regard,the configuration described above includes different kinds of map data.Thus, an appropriate mapping is selected depending on each situation.When different kinds of map data are provided, the structure of each ofdifferent kinds of mappings is simplified as compared to the structureof a single mapping in a case in which only the single mapping isprovided.

Aspect 6. In the catalyst deterioration detection device according toaspect 5, the different kinds of map data include data for each of areasthat are divided based on a flow rate of the fluid flowing into thecatalyst, and the selection process includes a process that selects themap data used to calculate the deterioration level variable of thecatalyst based on the flow rate.

Even when the air-fuel ratio of the mixture that is burned in acombustion chamber is the same, the excess amount of the actual fuel inrelation to the amount of fuel that reacts with oxygen contained in thefluid flowing into the catalyst without excess or deficiency may widelychange depending on the level of the flow rate of the fluid flowing intothe catalyst. Consequently, the oxygen storage amount of the catalystmay widely change per unit time. For this reason, the flow rate of thefluid flowing into the catalyst may significantly affect the time seriesdata of the downstream detection variable. For this reason, if a singlemap data is used regardless of whether the flow rate of the fluidflowing into the catalyst is large or small, the behavior of the timeseries data of the downstream detection variable, which changes inaccordance with the flow rate of the fluid, may need to be learned. As aresult, in a case in which a single map data is used regardless ofwhether the flow rate of the fluid flowing into the catalyst is large orsmall, the structure of the mapping becomes more complicated. In thisregard, in the configuration described above, different map data areused for each level of the flow rate of the fluid flowing into thecatalyst. Thus, the structure of the mapping is simplified.

Aspect 7. In the catalyst deterioration detection device according toaspect 5, the different kinds of map data include data for each of areasthat are divided based on a temperature of the catalyst, and theselection process includes a process that selects the map data used tocalculate the deterioration level of the catalyst based on thetemperature of the catalyst.

Since the maximum value of the oxygen storage amount of a catalystchanges in accordance with the temperature of the catalyst, even whencatalysts have the same catalyst deterioration level, if the catalystshave different temperatures, the behavior of the time series data of thedownstream detection variable differs between the catalysts. In otherwords, when the deterioration level of a first catalyst is the same asthe deterioration level of a second catalyst and the temperature of thefirst catalyst differs from the temperature of the second catalyst, thebehavior of the time series data of the downstream detection variablecorresponding to the first catalyst differs from the behavior of thetime series data of the downstream detection variable corresponding tothe second catalyst. For this reason, if the temperature-dependentdifferences in the behavior of the time series data of the downstreamdetection variable are configured to be distinguished, the structure ofthe mapping becomes more complicated. In this regard, in theconfiguration described above, different pieces map data are used foreach temperature of the catalyst. Thus, the structure of the mapping issimplified.

Aspect 8. In the catalyst deterioration detection device according toaspect 5, the different kinds of map data include different pieces ofdata corresponding to whether a fuel cutoff process is being executed,and the selection process includes a process that selects the map datain accordance with whether or not the fuel cutoff process is beingexecuted.

When the fuel cutoff process is being executed, a large amount of oxygenflows into the catalyst. For this reason, the behavior of the timeseries data of the downstream detection variable greatly differsdepending on whether or not the fuel cutoff process is being executed.Regardless of the difference, if the deterioration level variable iscalculated by a single map data, the structure of the mapping becomesmore complicated. In this regard, in the configuration described above,different map data are used in accordance with whether or not the fuelcutoff process is being executed. Thus, the structure of the mapping issimplified.

Aspect 9. In the catalyst deterioration detection device according toany one of aspects 1 to 5, the acquisition process includes a processthat acquires a variable that is used as an input to the mapping when apredetermined condition is satisfied, and the predetermined conditionincludes a condition indicating that a flow rate of the fluid flowinginto the catalyst is within a predetermined range.

Even when the air-fuel ratio of the mixture that is burned in acombustion chamber is the same, the excess amount of the actual fuel inrelation to the amount of fuel that reacts with oxygen contained in thefluid flowing into the catalyst without excess or deficiency greatlychanges depending on the level of the flow rate of the fluid flowinginto the catalyst. Consequently, the oxygen storage amount of thecatalyst widely changes per unit time depending on the level of the flowrate of the fluid flowing into the catalyst. For this reason, the flowrate of the fluid flowing into the catalyst may significantly affect thetime series data of the downstream detection variable. For this reason,if a single map data is used regardless of whether the flow rate of thefluid flowing into the catalyst is large or small, the behavior of thetime series data of the downstream detection variable, which changes inaccordance with the flow rate of the fluid, needs to be learned. Thus,the structure of the mapping becomes more complicated. In this regard,in the configuration described above, a sampling value corresponding towhen the flow rate of the fluid flowing into the catalyst is within thepredetermined range is used. Thus, the mapping is specialized in a casein which the flow rate is within the predetermined range, and thus, thestructure of the mapping is simplified. In other words, thedeterioration level variable of the catalyst is calculated based on thesampling value that is acquired when the flow rate of the fluid flowinginto the catalyst is within the predetermined range. Thus, the structureof the mapping is simplified as compared to a configuration in which thedeterioration level variable of the catalyst is calculated regardless ofthe flow rate of the fluid flowing into the catalyst.

Aspect 10. In the catalyst deterioration detection device according toany one of aspects 1 to 5, the acquisition process includes a processthat acquires a variable that is used as an input to the mapping when apredetermined condition is satisfied, and the predetermined conditionincludes a condition indicating that a temperature of the catalyst iswithin a predetermined range.

Since the maximum value of the oxygen storage amount of a catalystchanges in accordance with the temperature of the catalyst, even whencatalysts have the same catalyst deterioration level, if the catalystshave different temperatures, the behavior of the time series data of thedownstream detection variable differs between the catalysts. For thisreason, if the temperature-dependent differences in the behavior of thetime series data of the downstream detection variable are configured tobe distinguished, the structure of the mapping becomes more complicated.In this regard, in the configuration described above, a sampling valuecorresponding to when the temperature of the catalyst is within thepredetermined range is used. Thus, the structure of the mapping issimplified.

Aspect 11. In the catalyst deterioration detection device according toany one of aspects 1 to 5, the acquisition process includes a processthat acquires a variable that is used as an input to the mapping insynchronization with a point in time at which a predetermined conditionis satisfied, and the predetermined condition is a condition indicatingthat an amount of oxygen stored in the catalyst corresponds to a maximumvalue or a minimum value.

The behavior of the time series data of the downstream detectionvariable changes depending on the oxygen storage amount. As a result,the behavior of the time series data of the downstream detectionvariable differs depending on the oxygen storage amount corresponding towhen the oldest downstream detection variable was acquired among thetime series data of the downstream detection variables. For this reason,if a single mapping is used regardless of the oxygen storage amountcorresponding to when the oldest downstream detection variable wasacquired among the time series data of the downstream detectionvariables, the structure of the mapping becomes more complicated. Inthis regard, in the configuration described above, sampling is performedin synchronization with a point in time at which the oxygen storageamount reaches the maximum value or the minimum value. This allows fordetermination of a situation in which the time series data is acquired.As a result, the structure of the mapping is simplified. Moreover, thetime series data of the downstream detection variable is used as aninput. This limits or eliminates the need for waiting until a largeamount of oxygen or unburned fuel flows downstream of the catalyst inorder to clearly determine a point in time at which the oxygen storageamount of the catalyst is switched from the maximum value to zero orfrom zero to the maximum value.

Aspect 12. In the catalyst deterioration detection device according toany one of aspects 1 to 11, the dealing process includes a limitingprocess that limits an amount of unburned fuel flowing into the catalystto a reduced amount.

When the deterioration level is high and a large amount of unburned fuelflows into the catalyst, it is difficult to sufficiently oxidize theunburned fuel with oxygen in the catalyst. This may result in anincrease in the amount of unburned fuel flowing downstream of thecatalyst. In this regard, in the configuration described above, when thedeterioration level is higher than or equal to a predetermined level,the amount of unburned fuel flowing downstream of the catalyst islimited to a reduced amount. This limits an increase in the amount ofunburned fuel flowing downstream of the catalyst.

Aspect 13. A catalyst deterioration detection system includes theprocessing circuitry and the storage device according to any one ofaspects 1 to 11. The deterioration level variable calculation processincludes an oxygen storage amount calculation process that uses at leasta part of the map data to calculate a value corresponding to a maximumvalue of an oxygen storage amount of the catalyst. The processingcircuitry includes a first execution device and a second executiondevice. The first execution device is installed in a vehicle and isconfigured to execute the acquisition process, a vehicle sidetransmission process that transmits data acquired by the acquisitionprocess to an outside of the vehicle, a vehicle side reception processthat receives a signal based on a calculation result of the oxygenstorage amount calculation process, and the dealing process. The secondexecution device is disposed outside the vehicle and is configured toexecute an outside reception process that receives data transmitted bythe vehicle side transmission process, the oxygen storage amountcalculation process, and an outside transmission process that transmitsa signal based on a calculation result of the oxygen storage amountcalculation process to the vehicle.

In the configuration described above, the oxygen storage amountcalculation process is executed outside the vehicle. This reduces thecomputational load of on-board devices.

Aspect 14. A data analysis device includes the second execution deviceand the storage device according to aspect 13.

Aspect 15. A control device of an internal combustion engine, thecontrol device includes the first execution device according to aspect13.

Aspect 16. A method is for providing state information of a used vehicleon which an internal combustion engine is mounted. The internalcombustion engine is provided with a catalyst provided in an exhaustpassage. The method causes a computer to execute the acquisition processand the deterioration level variable calculation process according toany one of aspects 1 to 11, a storage process that stores a calculationresult of the deterioration level variable calculation process togetherwith a vehicle ID in a storage device, and an output process thatoutputs deterioration level information of the catalyst corresponding tothe vehicle ID in response to an access from outside.

The deterioration level of the catalyst is useful information indetermining how much it would cost, for example, to repair a usedvehicle after the vehicle is purchased. In the method described above,the calculation result of the deterioration level of the catalyst isstored together with the vehicle ID, and the information regarding thedeterioration level is output in response to an access from outside.This provides useful information regarding the state of the usedvehicle.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view illustrating the configuration of a control device anda drive system of a vehicle according to a first embodiment.

FIG. 2 is a block diagram illustrating some of processes executed by thecontrol device according to the first embodiment.

FIG. 3 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to the first embodiment.

FIG. 4 is a flowchart illustrating the sequence of a fail-safe processaccording to the first embodiment.

FIG. 5 is a diagram illustrating a system that generates map dataaccording to the first embodiment.

FIG. 6 is a flowchart illustrating the sequence of a map data learningprocess according to the first embodiment.

FIG. 7 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to a second embodiment.

FIG. 8 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to a third embodiment.

FIG. 9 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to a fourth embodiment.

FIG. 10 is a flowchart illustrating the sequence of a process thatselects map data according to a fifth embodiment.

FIG. 11 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to a sixth embodiment.

FIG. 12 is a flowchart illustrating the sequence of a process specifiedby a deterioration detection program according to a seventh embodiment.

FIG. 13 is a block diagram illustrating the configuration of a catalystdeterioration detection system and a dealer terminal according to aneighth embodiment.

FIG. 14 is a flowchart illustrating the sequence of a process executedby the catalyst deterioration detection system according to the eighthembodiment.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative size, proportions, and depiction of elements in thedrawings may be exaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

This description provides a comprehensive understanding of the methods,apparatuses, and/or systems described. Modifications and equivalents ofthe methods, apparatuses, and/or systems described are apparent to oneof ordinary skill in the art. Sequences of operations are exemplary, andmay be changed as apparent to one of ordinary skill in the art, with theexception of operations necessarily occurring in a certain order.Descriptions of functions and constructions that are well known to oneof ordinary skill in the art may be omitted.

Exemplary embodiments may have different forms, and are not limited tothe examples described. However, the examples described are thorough andcomplete, and convey the full scope of the disclosure to one of ordinaryskill in the art.

First Embodiment

Hereinafter, a first embodiment of a catalyst deterioration detectiondevice will be described with reference to the drawings.

In an internal combustion engine 10 installed in a vehicle VCillustrated in FIG. 1, a throttle valve 14 is provided in an intakepassage 12, and port injection valves 16 are provided downstream of thethrottle valve 14. As intake valves 18 open, air that is drawn from theintake passage 12 and fuel injected from the port injection valves 16flow into combustion chambers 20. The internal combustion engine 10 isprovided with in-cylinder injection valves 22, which directly injectfuel into the combustion chambers 20, and ignition devices 24 thatgenerate spark discharges. A mixture of air and fuel is provided forcombustion in the combustion chambers 20, and energy generated by thecombustion is output as rotational energy of a crankshaft 26. As exhaustvalves 28 open, the mixture provided for the combustion is discharged toan exhaust passage 30 as exhaust. The exhaust passage 30 is providedwith an upstream catalyst 32, which is a three-way catalyst and iscapable of storing oxygen, and a downstream catalyst 34 which is athree-way catalyst and is capable of storing oxygen. The exhaust passage30 is connected to the intake passage 12 via an exhaust gasrecirculation (EGR) passage 36. The EGR passage 36 is provided with anEGR valve 38 that regulates the flow path cross-sectional area of theEGR passage 36.

The rotational power of the crankshaft 26 is transmitted to an intakecamshaft 42 via an intake variable valve timing device 40. Therotational power of the crankshaft 26 is also transmitted to an exhaustcamshaft 46 via an exhaust variable valve timing device 44. The intakevariable valve timing device 40 changes a relative rotational phasedifference between the intake camshaft 42 and the crankshaft 26. Theexhaust variable valve timing device 44 changes a relative rotationalphase difference between the exhaust camshaft 46 and the crankshaft 26.

The crankshaft 26 of the internal combustion engine 10 is mechanicallycoupled to a carrier C of a planetary gear mechanism 50, whichconfigures a power split mechanism. The planetary gear mechanism 50includes a sun gear S that is mechanically coupled to a rotary shaft ofa motor-generator (MG) 52. The planetary gear mechanism 50 includes aring gear R that is mechanically coupled to a rotary shaft of amotor-generator 54 and drive wheels 56. Voltage is applied from abattery 62 to each terminal of the motor-generator 52 via an inverter58. Voltage is applied from the battery 62 to each terminal of themotor-generator 54 via an inverter 60.

The internal combustion engine 10 is controlled by a control device 70.The control device 70 operates operation units such as the throttlevalve 14, the port injection valve 16, the in-cylinder injection valve22, the ignition device 24, the EGR valve 38, the intake variable valvetiming device 40, and the exhaust variable valve timing device 44 tocontrol the control variables of the internal combustion engine 10 suchas the torque and the exhaust composition ratio. The control device 70also controls the motor-generator 52 and operates the inverter 58 tocontrol the torque and the rotational speed, which are control variablesof the motor-generator 52. The control device 70 also controls themotor-generator 54 and operates the inverter 60 to control the torqueand the rotational speed, which are control variables of themotor-generator 54. FIG. 1 shows operation signals MS1 to MS9 of thethrottle valve 14, the port injection valve 16, the in-cylinderinjection valve 22, the ignition device 24, the EGR valve 38, the intakevariable valve timing device 40, the exhaust variable valve timingdevice 44, and the inverters 58 and 60 respectively.

When the control device 70 controls the control variables, the controldevice 70 refers to an intake air amount Ga detected by an airflow meter80, an upstream detection value Afu, which is a detection value of anupstream air-fuel ratio sensor 82 provided upstream of the upstreamcatalyst 32, or a downstream detection value Afd, which is a detectionvalue of a downstream air-fuel ratio sensor 84 provided between theupstream catalyst 32 and the downstream catalyst 34. In addition, thecontrol device 70 refers to an output signal Scr of a crank angle sensor86, an accelerator pedal operating amount ACCP, which is the amount ofdepression of an accelerator pedal detected by an accelerator pedalsensor 88, or a vehicle speed SPD detected by a vehicle speed sensor 90.In addition, the control device 70 refers to an atmospheric pressure Padetected by an atmospheric pressure sensor 92, an ambient temperature TOdetected by an ambient temperature sensor 94, a charging-dischargingcurrent I of the battery 62 detected by a current sensor 96, and aterminal voltage V of the battery 62 detected by a voltage sensor 98.

The control device 70 includes a CPU 72, a ROM 74, a storage device 76,which is an electrically rewritable non-volatile memory, and peripheralcircuitry 77. These components are configured to communicate with eachother through a local network 78. The peripheral circuitry 77 includes,for example, a circuit that generates a clock signal to specify aninternal operation, a power supply circuit, and a reset circuit.

The control device 70 controls the above-described control variablesthrough programs stored in the ROM 74 and executed by the CPU 72.

FIG. 2 illustrates some of the processes implemented by the CPU 72executing the programs stored in the ROM 74.

A base injection amount calculation process M10 calculates a baseinjection amount Qb, which is the base value of a fuel amount that setsthe air-fuel ratio of the mixture in the combustion chamber 20 to atarget air-fuel ratio, based on a charging efficiency η. Morespecifically, for example, in a case in which the charging efficiency ηis expressed as a percentage, the base injection amount calculationprocess M10 may calculate the base injection amount Qb by multiplyingthe charging efficiency η by a fuel amount QTH per 1% of the chargingefficiency η, which sets the air-fuel ratio to the target air-fuelratio. The base injection amount Qb is an amount of fuel that iscalculated so that the air-fuel ratio is controlled to the targetair-fuel ratio based on the amount of air filling the combustion chamber20. The target air-fuel ratio is a stoichiometric air-fuel ratio. Thecharging efficiency η is a parameter that determines the amount airfilling the combustion chamber 20 and is calculated by the CPU 72 basedon a rotational speed NE and the intake air amount Ga. The rotationalspeed NE is calculated by the CPU 72 based on the output signal Scr ofthe crank angle sensor 86.

A feedback process M12 calculates and outputs a feedback correctionfactor KAF by adding one to a correction ratio 6 of the base injectionamount Qb, which is a feedback operating amount, that is, an operatingamount that causes the upstream detection value Afu to be a target valueAf* through feedback control. More specifically, the feedback processM12 sets the correction ratio 5 to the sum of output values of aproportional control element and a derivative control element, which usethe difference between the upstream detection value Afu and the targetvalue Af* as an input, and an output value of an integral controlelement, which retains and outputs an integrated value of the valuecorresponding to the differences.

A request injection amount calculation process M14 calculates a requestinjection amount Qd by multiplying the base injection amount Qb by thefeedback correction factor KAF.

An injection valve operation process M16 outputs the operation signalMS2 to the port injection valve 16 and outputs the operation signal MS3to the in-cylinder injection valve 22 based on the request injectionamount Qd to operate the port injection valve 16 and the in-cylinderinjection valve 22. More specifically, the injection valve operationprocess M16 sets an injection division ratio Kp to the ratio of a fuelinjection amount of the port injection valve 16 to the request injectionamount Qd and operates the port injection valve 16 and the in-cylinderinjection valve 22 in accordance with the injection division ratio Kp.

When the downstream detection value Afd is richer than a stoichiometricpoint Afs, which indicates the stoichiometric air-fuel ratio, by apredetermined amount εr or greater, a sub-feedback process M18 sets thetarget value Af* to be leaner than the stoichiometric point Afs by aspecified amount δ1. When the downstream detection value Afd is leanerthan the stoichiometric point Afs, which indicates the stoichiometricair-fuel ratio, by a predetermined amount ε1 or greater, thesub-feedback process M18 sets the target value Af* to be richer than thestoichiometric point Afs by a specified amount δr.

When release of the accelerator pedal is detected based on theaccelerator pedal operating amount ACCP and the rotational speed NE isgreater than or equal to a predetermined speed, a fuel cutoff processM19 stops the injection of fuel from the port injection valve 16 and thein-cylinder injection valve 22. The injection valve operation processM16 includes a process that performs an increase correction on therequest injection amount Qd during a predetermined period from when theprocess is returned from the fuel cutoff process.

An ignition process M20 outputs the operation signal MS4 to the ignitiondevice 24 to operate an ignition timing aig, which is an operatingamount of the ignition device 24. The ignition process M20 includes aprocess that normally sets a base ignition timing in accordance with therotational speed NE and the charging efficiency η to set the ignitiontiming aig based on the base ignition timing. The ignition process M20includes a process that sets the ignition timing aig based on anignition timing that is retarded from the base ignition timing by aretard amount Δaig during a warm-up process of the upstream catalyst 32.

A catalyst temperature calculation process M22 calculates a catalysttemperature Tcat, which is the temperature of the upstream catalyst 32,based on the rotational speed NE, the charging efficiency η, theignition timing aig, and the vehicle speed SPD. More specifically, thecatalyst temperature calculation process M22 is implemented in thefollowing manner. When the ROM 74 stores in advance map data that usesthe rotational speed NE and the charging efficiency η as input variablesand the base temperature as an output variable, the CPU 72 obtains abase temperature through map calculation. Also, when the ROM 74 storesin advance map data that uses the ignition timing aig as an inputvariable and the ignition timing correction amount, which is thecorrection amount of the base temperature based on the ignition timingaig, as an output variable, the CPU 72 obtains an ignition timingcorrection amount through map calculation. When the ROM 74 stores inadvance map data that uses the vehicle speed SPD as an input variableand the vehicle speed correction amount, which is the correction amountof the base temperature based on the vehicle speed SPD, as an outputvariable, the CPU 72 obtains a vehicle speed correction amount throughmap calculation. Then, the CPU 72 corrects the base temperature usingthe ignition timing correction amount and the vehicle speed correctionamount to calculate the catalyst temperature Tcat.

The map data is a data set of discrete values of an input variable andthe value of an output variable corresponding to each value of the inputvariable. For example, when the value of an input variable matches anyone of the values of the input variable in the map data, the mapcalculation may use the value of the corresponding output variable inthe map data as the calculation result. When the value of the inputvariable does not match any one of the values of the input variable inthe map data, the map calculation may use a value that is obtained byinterpolating multiple values of the output variable in the map data asthe calculation result.

An in-catalyst flow rate calculation process M24 calculates anin-catalyst flow rate CF, which is the volumetric flow rate of a fluidflowing through the upstream catalyst 32, based on the rotational speedNE and the charging efficiency η. More specifically, this process isimplemented in the following manner. More specifically, the CPU 72calculates the mass flow rate of the fluid flowing into the upstreamcatalyst 32 based on the charging efficiency η and the rotational speedNE. The CPU 72 estimates the pressure and temperature of the fluidflowing into the upstream catalyst 32 based on the rotational speed NEand the charging efficiency η, and converts the mass flow rate into avolumetric flow rate based on the estimated pressure and temperature.Then, the CPU 72 calculates the in-catalyst flow rate CF by convertingthe converted volumetric flow rate into a volumetric flow rate in theupstream catalyst 32 based on the ratio of the flow path cross-sectionalarea of the upstream catalyst 32 to the flow path cross-sectional areaof the exhaust passage 30 upstream of the upstream catalyst 32.

An output control process M30 calculates an output Peg requested to themotor-generators 52 and 54 and an output Peg requested to the internalcombustion engine 10 based on the accelerator pedal operating amountACCP and the vehicle speed SPD. In particular, when a state of chargeSOC of the battery 62 is greater than or equal to a predetermined valueSth, the output control process M30 selects an EV mode in which theoutput Pmg of the internal combustion engine 10 is set to zero. When thestate of charge SOC is less than the predetermined value Sth, the outputcontrol process M30 selects an EHV mode in which the internal combustionengine 10 and the motor-generators 52 and 54 cooperate to ensure therequested output. The state of charge SOC is calculated by the CPU 72based on the terminal voltage V or the charging-discharging current I.More specifically, for example, when the absolute value of thecharging-discharging current I is negligibly small, the CPU 72 assumesthe terminal voltage V to be the open end voltage, and calculates thestate of charge SOC based on a relationship between the open end voltageand the state of charge SOC. When the absolute value of thecharging-discharging current I is large, the state of charge SOC isupdated by the charging-discharging current I.

An MG control process M32 outputs the operation signals MS8 and MS9 tothe inverters 58 and 60 so that the sum of outputs of themotor-generators 52 and 54 becomes the output Pmg.

A throttle operation process M34 outputs the operation signal MS1 to thethrottle valve 14 based on the output Peg so that the output of theinternal combustion engine 10 becomes the output Peg.

In addition to the processes illustrated in FIG. 2, the control device70 executes a process that calculates the deterioration level of theupstream catalyst 32. Hereinafter, the process will be described indetail with reference to FIG. 3 and other drawings.

The process illustrated in FIG. 3 is implemented, for example, by theCPU 72 repeatedly executing a deterioration detection program 74 astored in the ROM 74 illustrated in FIG. 1 at predetermined timeintervals. In the following description, the number of each step isrepresented by the letter S followed by a numeral.

At the start of a series of steps illustrated in FIG. 3, the CPU 72acquires time series data for each of the upstream average value Afuave,a downstream average value Afdave, the in-catalyst flow rate CF, therotational speed NE, the charging efficiency η, and the catalysttemperature Tcat in a predetermined period (S10). Hereinafter, points intime of sampling are denoted by “1, 2, . . . , and sn” in order from theearliest one. For example, the time series data of the upstream averagevalue Afuave are expressed as “Afuave (1) to Afuave (sn)”. The term “sn”refers to the number of pieces of data included in the time series dataof each variable. More specifically, the above-described predeterminedperiod is set to a period in which “sn” pieces of data are sampled foreach of the above-described variables. The above-described predeterminedperiod is determined from a sampling time interval and the number ofpieces of data “sn” and is not determined by a value such as thedownstream detection value Afd.

The upstream average value Afuave is the average value of the upstreamdetection values Afu at an interval of the above-described time seriesdata sampling. More specifically, the CPU 72 samples the upstreamdetection value Afu a number of times at an interval of the time seriesdata sampling and calculates the average value of the upstream detectionvalues Afu to set the average value to the upstream average valueAfuave. In the same manner, the downstream average value Afdave is theaverage value of the downstream detection values Afd at an interval ofthe above-described time series data sampling.

Subsequently, the CPU 72 assigns the values that are acquired by stepS10 to input variables x (1) to x (6sn) of a mapping that outputs adeterioration level variable Rd, which is a variable indicating thedeterioration level of the upstream catalyst 32 (S12). Morespecifically, when m=1 to sn, the CPU 72 assigns an upstream averagevalue Afuave (m) to an input variable x (m), assigns a downstreamaverage value Afdave (m) to an input variable x (sn+m), assigns anin-catalyst flow rate CF (m) to an input variable x (2sn+m), and assignsa rotational speed NE (m) to an input variable x (3sn+m). In addition,the CPU 72 assigns a charging efficiency η (m) to an input variable x(4sn+m) and assigns a catalyst temperature Tcat (m) to an input variablex (5sn+m).

Subsequently, the CPU 72 calculates the deterioration level variable Rd,which is the output value of a mapping, by inputting the input variablesx (1) to x (6sn) to a mapping that is specified by a map data 76 astored in the storage device 76 illustrated in FIG. 1 (S14). In thedescription, a calculation of the output value of a mapping refers to acalculation of a variable, i.e. a calculation of the value of thevariable. In the present embodiment, the deterioration level variable Rdis quantified in the following manner.

Rd=1−RR

RR=(the maximum value of an actual oxygen storage amount of the upstreamcatalyst 32 at a predetermined temperature)/(the maximum value of theoxygen storage amount of a reference catalyst at the predeterminedtemperature)

Therefore, the larger the value of the deterioration level variable Rdis, the larger the deterioration level is. In particular, when themaximum value of the oxygen storage amount of the upstream catalyst 32is equal to the maximum value of the oxygen storage amount of thereference catalyst, the deterioration level variable Rd is zero.

In the present embodiment, the mapping is configured by a neural networkin which the number of intermediate layers is “a,” activation functionsh1 to hα of the intermediate layers are hyperbolic tangents, andactivation function f of an output layer is a ReLU. Here, ReLU is afunction that outputs the non-lesser one of an output and zero. Forexample, the node values of a first intermediate layer are generated byinputting the input variables x (1) to x (6sn) to a linear mappingspecified by a factor w(1)ji (j=0 to n1 and i=0 to 6sn) to obtainoutputs and inputting the outputs to the activation function h1. Morespecifically, when m=1, 2, . . . , and a, the node values of an m-thintermediate layer are generated by inputting the outputs of a linearmapping that is specified by a factor w(m) to an activation function hm.Here, n1, n2, . . . , and nα refers to the number of nodes in the first,a second, . . . , and an α-th intermediate layers. In addition, w(1)j0is one of bias parameter, and an input variable x (0) is defined as one.

Subsequently, the CPU 72 determines whether or not the deteriorationlevel variable Rd is greater than or equal to a specified value RdthH(S16). When it is determined that the deterioration level variable Rd isgreater than or equal to the specified value RdthH (S16: YES), in orderto prompt the user to perform repair, the CPU 72 executes a notificationprocess that operates a warning lamp 99 illustrated in FIG. 1 to issue anotification (S18).

When it is determined that the deterioration level variable Rd is lessthan the specified value RdthH (S16: NO), the CPU 72 determines whetheror not the deterioration level variable Rd is greater than or equal to apredetermined value RdthL (S20). The predetermined value RdthL is avalue smaller than the specified value RdthH. When it is determined thatthe deterioration level variable Rd is greater than or equal to thepredetermined value RdthL (S20: YES), the CPU 72 sets a fail flag F toone (S22). When step S18 is performed, it is assumed that the fail flagF is already set to one. When it is determined that the deteriorationlevel variable Rd is less than the predetermined value RdthL (S20: NO),the CPU 72 assigns zero to the fail flag F (S24).

When steps S18, S22, and S24 are completed, the CPU 72 temporarily endsthe series of steps illustrated in FIG. 3.

FIG. 4 illustrates the sequence of steps other than step S18 in aprocess that deals with the deterioration of the upstream catalyst 32.The process illustrated in FIG. 4 is implemented, for example, by theCPU 72 repeatedly executing a fail-safe program 74 b stored in the ROM74 illustrated in FIG. 1 at predetermined time intervals.

At the start of a series of steps illustrated in FIG. 4, the CPU 72determines whether or not the fail flag F is one (S30). When it isdetermined that the fail flag F is zero (S30: NO), the CPU 72 assigns areference value Sth0 to the predetermined value Sth used in the outputcontrol process M30, assigns a reference amount δI0 to the specifiedamount δI and assigns a reference amount δr0 to the specified amount δrin the sub-feedback, and assigns a reference amount Δaig0 to the retardamount Δaig for a catalyst warm-up process (S32).

In contrast, when it is determined that the fail flag F is one (S30:YES), the CPU 72 obtains a value by performing an increase correction onthe reference value Sth0 using a predetermined amount ΔSth and assignsthe obtained value to the predetermined value Sth (S34). Taking intoconsideration that the capacity of the upstream catalyst 32 foroxidizing unburned fuel is lowered due to the deterioration, thisprocess maximizes the stop state of the internal combustion engine 10 sothat the amount of unburned fuel flowing into the upstream catalyst 32is limited to a reduced amount.

The CPU 72 obtains a value by performing a decrease correction on thereference amount δI0 using a correction amount ΔδI and assigns theobtained value to the specified amount δI, and obtains a value byperforming a decrease correction on the reference amount δr0 using acorrection amount Δδr and assigns the obtained value to the specifiedamount δr (S36). Taking into consideration that the capacity of theupstream catalyst 32 for oxidizing unburned fuel is lowered due to thedeterioration, this process limits the amount of unburned fuel flowinginto the upstream catalyst 32 to a reduced amount. More specifically, ina case in which the fail flag F is one, the specified amount δr is setto be a smaller value than in a case in which the fail flag F is zero.This lowers the enrichment level when the target value Af* is set to bericher than the stoichiometric point Afs. For this reason, the amount ofunburned fuel in the exhaust flowing into the upstream catalyst 32 perunit time is smaller than that in a case in which the fail flag F iszero. This limits the flow of unburned fuel toward a downstream side ofthe upstream catalyst 32.

In addition, the CPU 72 obtains a value by performing an increasecorrection on the reference amount Δaig0 using a correction amount ΔFand assigns the obtained value into the retard amount Δaig (S38). Whenthe upstream catalyst 32 deteriorates, the exhaust purificationperformance of the upstream catalyst 32 is lower than when the upstreamcatalyst 32 does not deteriorate. Thus, the above-described processreduces the ratio of conversion of the combustion energy of the mixtureinto torque to increase the temperature of exhaust discharged to theexhaust passage 30 so that the upstream catalyst 32 is wormed up at anearly time.

When steps S32 and S38 are completed, the CPU 72 temporarily ends theseries of steps illustrated in FIG. 4.

The process of generating the map data 76 a will now be described.

FIG. 5 illustrates a system which generates the map data 76 a.

As illustrated in FIG. 5, in the present embodiment, a dynamometer 100is mechanically coupled to the crankshaft 26 of the internal combustionengine 10. When the internal combustion engine 10 runs, a sensor group102 detects various state variables of the internal combustion engine10. The detection results are input to an adaptation device 104, whichis a computer that generates the map data 76 a. The sensor group 102includes sensors that detect values for generating inputs to a mappingsuch as the upstream air-fuel ratio sensor 82, the downstream air-fuelratio sensor 84, and the crank angle sensor 86.

FIG. 6 illustrates the sequence of generating map data. The processillustrated in FIG. 6 is executed by the adaptation device 104. Theprocess illustrated in FIG. 6 may be implemented, for example, byproviding the adaptation device 104 with a CPU and a ROM and causing theCPU to execute programs stored in the ROM.

At the start of a series of steps illustrated in FIG. 6, based on thedetection results of the sensor group 102, the adaptation device 104acquires the same data as that acquired in step S10, as training data(S40). In a state in which two or more upstream catalysts 32 havingdeterioration level variables Rd that differ from each other, which areindividually measured in advance, are prepared and one of the upstreamcatalysts 32 is selectively installed in the internal combustion engine10, the process described above is executed so that the installedupstream catalyst 32 has a deterioration level variable Rdt that is usedas teacher data.

In accordance with the procedure of step S12, the adaptation device 104assigns the training data other than the teacher data to the inputvariables x (1) to x (6sn) (S42). Then, in accordance with the procedureof step S14, the adaptation device 104 calculates the deteriorationlevel variable Rd by inputting the input variables x (1) to x (6sn)obtained by step S42 into a mapping (S44). Then, the CPU 72 determineswhether or not the number of samples of the deterioration levelvariables Rd calculated by step S44 is greater than or equal to apredetermined number (S46). Here, in order to obtain the predeterminednumber of samples or greater, the deterioration level variable Rd needsto be calculated two times or more for each of the above-describedupstream catalysts 32. Furthermore, the deterioration level variable Rdneeds to be calculated at various operating points that are specified bythe rotational speed NE and the charging efficiency η in accordance withchanges in the operation mode of the internal combustion engine 10.

When it is determined that the number of samples is not greater than orequal to the predetermined number (S46: NO), the adaptation device 104returns to step S40. When it is determined that the number of samples isgreater than or equal to the predetermined number (S46: YES), the CPU 72updates factors w(1)ji, w(2)kj, . . . , and w(α)1p to minimize the sumof the squares of the differences between the deterioration levelvariable Rdt as the teacher data and the deterioration level variable Rdcalculated by step S44 (S48). Then, the adaptation device 104 stores thefactors w(1)ji, w(2)kj, . . . , and w(α)1p as the map data 76 a that islearned (S50).

The operation and advantages of the present embodiment will now bedescribed.

The map data 76 a is learned to specify a mapping that uses the timeseries data of each of the upstream average value Afuave, the downstreamaverage value Afdave, the in-catalyst flow rate CF, the rotational speedNE, the charging efficiency η, and the catalyst temperature Tcat, asinputs to output the deterioration level variable Rd. The rotationalspeed NE and the charging efficiency r), which specify the operatingpoints of the internal combustion engine 10, may be regarded as the flowrate variables indicating the flow rate of the fluid which flows intothe upstream catalyst 32. The upstream average value Afuave is avariable indicating the ratio of the amount of actual fuel to the amountof fuel that reacts with oxygen contained in the fluid flowing into theupstream catalyst 32 without excess or deficiency. The amount of fuelthat reacts with oxygen contained in the fluid flowing into the upstreamcatalyst 32 without excess or deficiency is referred to as the idealfuel amount. Therefore, the upstream average value Afuave, therotational speed NE, and the charging efficiency η collectivelyconfigure an excess amount variable, that is, a variable correspondingto an excess amount of the actual fuel in relation to the amount of fuelthat reacts with oxygen contained in the fluid flowing into the upstreamcatalyst 32 without excess or deficiency. The excess amount may have anegative value. In other words, the excess amount may have a valueobtained by multiplying “−1” by the deficient amount of the actual fuelin relation to the amount of fuel that reacts with oxygen contained inthe fluid flowing into the upstream catalyst 32 without excess ordeficiency.

The behavior of the downstream average value Afdave changes inaccordance with the maximum value of the oxygen storage amount of theupstream catalyst 32 in addition to the above-described excess amount inthe fluid flowing into the upstream catalyst 32. The maximum value ofthe oxygen storage amount changes in accordance with not only thedeterioration level of the upstream catalyst 32 but also the temperatureof the upstream catalyst 32. Therefore, it may be assumed that thedeterioration level variable Rd of the upstream catalyst 32 iscalculated by inputting time series data indicating the behavior of thedownstream average value Afdave together with the time series data ofthe excess amount variable and the time series data of the catalysttemperature Tcat. As described above, the present embodiment does notuse a mapping that is learned through machine learning by inputting alarge number of various random variables of the internal combustionengine 10 to calculate the deterioration level variable Rd. Instead, inthe present embodiment, the variables that are input to the mapping arecarefully selected based on their relevance to the control of theinternal combustion engine 10. For this reason, the number of theintermediate layers of the neural network and the number of data sn ofthe time series data are reduced as compared to a case in which thevariables that are input to the mapping are not carefully selected basedon their relevance to the control of the internal combustion engine 10.In this way, the structure of the mapping that calculates thedeterioration level variable Rd may be simplified.

In particular, the behavior of the downstream average value Afdave forthe excess amount variable and the catalyst temperature Tcat isrecognized from the time series data of these variables. Thus, thedeterioration level variable Rd is calculated without directly detectingthat the oxygen storage amount of the upstream catalyst 32 has reachedzero or the maximum value. Since the deterioration level variable Rd iscalculated without changing the target value Af* for the detection ofthe deterioration level, the accumulated amount of deviation of thecomposition of the fluid flowing into the upstream catalyst 32 from thecomposition that is appropriate for the purification performance of theupstream catalyst 32 is reduced.

The present embodiment described above further obtains the followingadvantages.

(1) The input to the mapping includes the upstream average value Afuave.As compared to a case in which the upstream detection value Afu for eachtime interval of the time series data is used, further accurateinformation regarding oxygen and unburned fuel flowing into the upstreamcatalyst 32 is obtained without increasing the number of data pieces ofthe time series data. Ultimately, the deterioration level variable Rd iscalculated with higher accuracy.

(2) The input to the mapping includes the downstream average valueAfdave. As compared to a case in which the downstream detection valueAfd for each time interval of the time series data is used, furtheraccurate information regarding oxygen and unburned fuel flowing out fromthe upstream catalyst 32 is obtained without increasing the number ofdata pieces of the time series data. Ultimately, the deterioration levelvariable Rd is calculated with higher accuracy.

(3) The input to the mapping includes the rotational speed NE and thecharging efficiency η, which are used as operating point variablesspecifying the operating points of the internal combustion engine 10.The operating amounts of the operation units such as the ignition device24, the EGR valve 38, and the intake variable valve timing device 40 ofthe internal combustion engine 10 tend to be determined based on theoperating point of the internal combustion engine 10. For this reason,the operating point variable is a variable that not only configures theflow rate variable, together with the upstream average value Afuave, butalso includes information related to the operating amount of eachoperation unit. Therefore, the deterioration level variable Rd iscalculated based on the information related to the operating amount ofeach operation unit by inputting the operating point variable to themapping. Ultimately, the deterioration level variable Rd is calculatedwith higher accuracy.

(4) The input to the mapping includes the in-catalyst flow rate CF. Thein-catalyst flow rate CF is a variable that affects the rate of reactionbetween unburned fuel and oxygen in the upstream catalyst 32. For thisreason, when the in-catalyst flow rate CF is input to the mapping, thedeterioration level variable Rd is calculated with higher accuracy. Inthe present embodiment, the in-catalyst flow rate CF is calculated fromthe operating point variables of the internal combustion engine 10. Inprinciple, when the input to the mapping includes the operating pointvariables, the effect of the in-catalyst flow rate CF on the rate ofreaction between unburned fuel and oxygen in the upstream catalyst 32 isreflected during calculation of the deterioration level variable Rdwithout using the in-catalyst flow rate CF. However, to accomplish suchcalculation, the number of intermediate layers in the neural network andthe number sn of pieces of data in the time series data tend toincrease. In this regard, in the present embodiment, since the input tothe mapping includes the in-catalyst flow rate CF, the structure of themapping may be simplified.

Second Embodiment

Hereinafter, a second embodiment will be described with reference to thedrawings focusing on the differences from the first embodiment.

In the present embodiment, the output of the neural network is a maximumvalue Cmax of the current oxygen storage amount of the upstream catalyst32.

FIG. 7 illustrates the sequence of a process executed by the controldevice 70 in the present embodiment. The process illustrated in FIG. 7is implemented, for example, by the CPU 72 repeatedly executing thedeterioration detection program 74 a stored in the ROM 74 illustrated inFIG. 1 at predetermined time intervals. For the sake of convenience, inthe process illustrated in FIG. 7, the same step numbers are given tothe steps corresponding to the steps illustrated in FIG. 3.

At the start of a series of steps illustrated in FIG. 7, when step S10is completed, the CPU 72 inputs the time series data other than thecatalyst temperature Tcat to the input variables x (1) to x (5sn) of themapping (S12 a), instead of executing step S12. The same variables asused in step S12 are assigned to the input variables x (1) to x (5sn).

The CPU 72 calculates the maximum value Cmax through a neural networkthat uses the input variables x (1) to x (5sn) as inputs and uses themaximum value Cmax as an output (S14 a). The CPU 72 uses map data thatuses a catalyst temperature average value Tcatave, which is the averagevalue of the catalyst temperatures Tcat acquired in S10, and the maximumvalue Cmax as input variables and uses the deterioration level variableRd as an output variable to obtain the deterioration level variable Rdthrough map calculation (S52).

The CPU 72 proceeds to step S16.

In the present embodiment, the factors w(1)ji, w(2)kj, . . . , andw(α)1p of the neural network may be learned using the following teacherdata. More specifically, the relationship between the temperature andthe maximum value Cmax of each of the above-described upstream catalysts32 may be measured in advance. In a step corresponding to the stepillustrated in FIG. 6, the current maximum value Cmax of thecorresponding one of the target upstream catalysts 32 may be obtainedand used as teacher data.

Third Embodiment

Hereinafter, a third embodiment will be described with reference to thedrawings focusing on the differences from the first embodiment.

In the present embodiment, average values of the time series data ofeach of the in-catalyst flow rate CF, the rotational speed NE, thecharging efficiency η, and the catalyst temperature Tcat are calculatedand used as inputs to the mapping. Hereinafter, in FIG. 8, it is assumedin the present embodiment that the number sn of pieces of data in thetime series data of each variable acquired by step S10 is a multiple offive.

FIG. 8 illustrates the sequence of a process executed by the controldevice 70 in the present embodiment. The process illustrated in FIG. 8is implemented, for example, by the CPU 72 repeatedly executing thedeterioration detection program 74 a stored in the ROM 74 illustrated inFIG. 1 at predetermined time intervals. For the sake of convenience, inthe process illustrated in FIG. 8, the same step numbers are given tothe steps corresponding to the steps illustrated in FIG. 3.

In a series of the steps illustrated in FIG. 8, when step S10 iscompleted, the CPU 72 calculates an average value of each set of “sn/5”elements in order from the earliest element in each of the in-catalystflow rates CF, the rotational speeds NE, the charging efficiencys η, andthe catalyst temperatures Tcat (S60). More specifically, for example, anin-catalyst flow rate average value CFave (1) is calculated as theaverage value of in-catalyst flow rates CF (1), CF (2), . . . , and CF(sn/5). An in-catalyst flow rate average value CFave (2) is calculatedas the average value of in-catalyst flow rates CF ((sn/5)+1), CF((sn/5)+2), . . . , and CF (2n/5). In this manner, time series dataincluding five sets of in-catalyst flow rate average values CFave, timeseries data including five sets of rotational speed average valuesNEave, time series data including five sets of charging efficiencyaverage values ηave, and time series data including five sets ofcatalyst temperature average values Tcatave are produced.

Subsequently, the CPU 72 assigns the time series data generated by stepS60 and the time series data of each of the upstream average valueAfuave and the downstream average value Afdave acquired in step S10 tothe input variable x of the mapping (S12 b). More specifically, when m=1to sn, the CPU 72 assigns the upstream average value Afuave (m) to theinput variable x (m), and assigns the downstream average value Afdave(m) to the input variable x (sn+m). In addition, when m=1 to 5, the CPU72 assigns an in-catalyst flow rate average value CFave (m) to the inputvariable x (2sn+m), and assigns a rotational speed average value NEave(m) to an input variable x (2sn+5+m). In addition, the CPU 72 assigns acharging efficiency average value ηave (m) to an input variable x(2sn+10+m), and assigns a catalyst temperature average value Tcatave (m)to an input variable x (2sn+15+m).

Then, the CPU 72 calculates the deterioration level variable Rd througha neural network that uses the input variables x (1) to x (2sn+20)generated in S12 b as inputs and uses the deterioration level variableRd as an output (S14 b). The factor w(1)ji is where “i=0 to 2sn+20”.

When step S14 b is completed, the CPU 72 proceeds to step S16.

As described above, according to the present embodiment, the in-catalystflow rate average value CFave, the rotational speed average value NEave,the charging efficiency average value ηave, and the catalyst temperatureaverage value Tcatave are used as the inputs to the mapping. Thisreduces the dimensions of the input to the mapping. In the presentembodiment, the excess amount variable is collectively configured by thetime series data of the upstream average value Afuave, the time seriesdata of the rotational speed average value NEave, and the time seriesdata of the charging efficiency average value ηave. The rotational speedaverage value NEave and the charging efficiency average value ηaverepresent the operating points of the internal combustion engine 10 in aperiod in which “sn/5” sets of the upstream average values Afuave aresampled.

Fourth Embodiment

Hereinafter, a fourth embodiment will be described with reference to thedrawings focusing on the differences from the first embodiment.

In the present embodiment, as the map data 76 a, the storage device 76stores different map data for each of the operating point of theinternal combustion engine 10 and the catalyst temperature Tcat.

FIG. 9 illustrates the sequence of a process executed by the controldevice 70 in the present embodiment. The process illustrated in FIG. 9is implemented, for example, by the CPU 72 repeatedly executing thedeterioration detection program 74 a stored in the ROM 74 illustrated inFIG. 1 at predetermined time intervals. For the sake of convenience, inthe process illustrated in FIG. 9, the same step numbers are given tothe steps corresponding to the steps illustrated in FIG. 3.

In a series of the steps illustrated in FIG. 9, first, the CPU 72selects map data for calculating the deterioration level variable Rd inaccordance with the rotational speed NE and the charging efficiency n,which specify the operating point of the internal combustion engine 10,and the catalyst temperature Tcat (S62). This process can beimplemented, for example, when the ROM 74 stores in advance map datathat uses the rotational speed NE, the charging efficiency η, and thecatalyst temperature Tcat as input variables and uses the variablespecifying the map data as an output variable, by the CPU 72 obtaining avariable that specifies the map data through map calculation. The CPU 72acquires time series data of each of the upstream average value Afuaveand the downstream average value Afdave (S10 c).

Subsequently, the CPU 72 assigns the time series data acquired in stepS10 c to the input variable x of the mapping that is specified by themap data selected by step S62 (S12 c). More specifically, when m=1 tosn, the CPU 72 assigns the upstream average value Afuave (in) to theinput variable x (m), and assigns the downstream average value Afdave(m) to the input variable x (sn+m). The CPU 72 calculates thedeterioration level variable Rd through a neural network that uses theinput variables x (1) to x (2sn) generated in step S12 c as inputs anduses the deterioration level variable Rd as an output (S14 c). Thefactor w(1)ji is where “i=0 to 2sn”.

When step S14 c is completed, the CPU 72 proceeds to step S16.

In the learning of map data that is used when the operating point iswithin a predetermined range and the catalyst temperature Tcat is withina predetermined range, only training data in which the operating pointis within the predetermined range and the catalyst temperature Tcat iswithin the predetermined range is used.

As described above, in the present embodiment, the deterioration levelvariable Rd is calculated using different map data in accordance withthe operating point of the internal combustion engine 10 and thecatalyst temperature Tcat. Thus, when the operating point of theinternal combustion engine 10 varies widely, the CPU 72 calculates thedeterioration level variable Rd using different map data. Therefore,when a mapping specified by a single piece of map data is used, the flowrate of the fluid flowing into the upstream catalyst 32 does not widelychange. In this case, the excess amount of an actual fuel in relation tothe amount of fuel that reacts with oxygen contained in the fluidflowing into the upstream catalyst 32 without excess or deficiency maybe recognized simply from the upstream average value Afuave. Morespecifically, the above-described excess amount variable may beconfigured by only the upstream average value Afuave. In addition, whenthe mapping specified by a single piece of map data is used, thecatalyst temperature Tcat does not greatly change. In this case, in asituation in which the mapping specified by a single piece of map datais used, when the maximum value Cmax of the oxygen storage amount issmall, the deterioration level of the upstream catalyst 32 is largerthan when the maximum value Cmax is large. More specifically, thedeterioration level is quantified in accordance with the maximum valueCmax at the current temperature.

For this reason, in the present embodiment, the dimensions of thevariables that are input to the mapping are reduced. As a result, thenumber nα of the intermediate layers is reduced. Therefore, in thepresent embodiment, the structure of the mapping is simplified.

Fifth Embodiment

Hereinafter, a fifth embodiment will be described with reference to thedrawings focusing on the differences from the first embodiment.

In the present embodiment, the storage device 76 stores three types ofmap data as the map data 76 a.

FIG. 10 illustrates the sequence of a process that selects any one ofthe three types of map data to calculate the deterioration levelvariable Rd. The process illustrated in FIG. 10 is implemented, forexample, by the CPU 72 repeatedly executing the deterioration detectionprogram 74 a stored in the ROM 74 illustrated in FIG. 1 at predeterminedtime intervals.

In a series of steps illustrated in FIG. 10, first, the CPU 72determines whether or not the fuel cutoff process is being executed(S70). Then, when it is determined that the fuel cutoff process is beingexecuted (S70: YES), the CPU 72 selects first map data (S74). The firstmap data is map data that is dedicated to when the fuel cutoff processis executed, and is learned by using data in the fuel cutoff process astraining data.

When it is determined that the fuel cutoff process is not being executed(S70: NO), the CPU 72 determines whether or not it is within theabove-described predetermined period after the fuel cutoff process(S72). Then, when it is determined that it is within the predeterminedperiod (S72: YES), the CPU 72 selects a second map data (S76). Thesecond map data is learned by using time series data that is sampled inthe predetermined period after the fuel cutoff process as training data.More specifically, the second map data is learned by using time seriesdata that is sampled during a period in which an increase correction isperformed on the request injection amount Qd as training data.

When it is determined that it is not within the predetermined period(S72: NO), the CPU 72 selects a third map data (S78). The third map datais learned by using time series data that is sampled in a period thatexcludes the fuel cutoff process and the predetermined period after thefuel cutoff process as training data.

When steps S74, S76, and S78 are completed, the CPU 72 temporarily endsthe series of steps illustrated in FIG. 10.

As described above, in the present embodiment, the deterioration levelvariable Rd is calculated by using different map data for when the fuelcutoff process is being executed, the predetermined period after thefuel cutoff process, and the period outside of these specificallydescribed periods. During the fuel cutoff process, a large amount ofoxygen flows into the upstream catalyst 32, and the air-fuel ratio ofthe mixture, which is subject to combustion, tends to be disturbed inthe predetermined period after the fuel cutoff process. For this reason,as compared to a case in which single map data is used for thesedifferent situations, the structure of the mapping may be simplified.

Sixth Embodiment

Hereinafter, a sixth embodiment will be described with reference to thedrawings focusing on the differences from the fourth embodiment.

The present embodiment aims to simplify the structure of the mapping bylimiting conditions that allow the sampling of variables that are usedto calculate the deterioration level variable Rd.

FIG. 11 illustrates the sequence of a process executed by the controldevice 70 in the present embodiment. The process illustrated in FIG. 11is implemented, for example, by the CPU 72 repeatedly executing thedeterioration detection program 74 a stored in the ROM 74 illustrated inFIG. 1 at predetermined time intervals. For the sake of convenience, inthe process illustrated in FIG. 11, the same step numbers are given tothe steps corresponding to the steps illustrated in FIG. 3.

In a series of the steps illustrated in FIG. 11, the CPU 72 determineswhether or not the logical product of the following conditions (A) to(C) is true (S80).

Condition (A) is a condition indicating that the rotational speed NE isgreater than or equal to a first speed NEL and less than or equal to asecond speed NEH.

Condition (B) is a condition indicating that the charging efficiency r1is greater than or equal to a first charging efficiency ηL and less thanor equal to a second charging efficiency ηH.

Condition (C) is a condition indicating that the catalyst temperatureTcat is greater than or equal to a first temperature TcatL and less thanor equal to a second temperature TcatH.

When it is determined that the above-described logical product is true(S80: YES), the CPU 72 determines that the sampling of inputs to themapping is allowed and executes the steps following S10 c illustrated inFIG. 9.

As described above, in the present embodiment, the deterioration levelvariable Rd is calculated only in a case in which the operating point ofthe internal combustion engine 10 is within in the predetermined rangeand the catalyst temperature Tcat is within the predetermined range. Thelimitation imposed on the range of the operating point allowscalculation of the deterioration level variable Rd only in a case inwhich the flow rate of the fluid flowing into the upstream catalyst 32does not widely differ from a reference value. In this case, an excessamount of an actual fuel in relation to the amount of fuel that reactswith oxygen contained in the fluid flowing into the upstream catalyst 32without excess or deficiency may be recognized simply from the upstreamaverage value Afuave. More specifically, the above-described excessamount variable may be configured only by the upstream average valueAfuave. In addition, the limitation imposed on the range of the catalysttemperature Tcat allows calculation of the deterioration level variableRd only in a case in which the catalyst temperature Tcat does not widelydiffer from a reference temperature. In this case, when the maximumvalue Cmax of the oxygen storage amount is small, the deteriorationlevel of the upstream catalyst 32 is larger than when the maximum valueCmax is large. More specifically, the deterioration level is quantifiedin accordance with the maximum value Cmax at the current temperature.

For this reason, in the present embodiment, the dimensions of thevariables that are input to the mapping are reduced. As a result, thenumber nα of the intermediate layers is reduced. Therefore, in thepresent embodiment, the structure of the mapping may be simplified.

Seventh Embodiment

Hereinafter, a seventh embodiment will be described with reference tothe drawings focusing on the differences from the first embodiment.

The present embodiment aims to simplify the structure of the mapping bylimiting conditions that allow the sampling of variables that are usedto calculate the deterioration level variable Rd.

FIG. 12 illustrates the sequence of a process executed by the controldevice 70 in the present embodiment. The process illustrated in FIG. 12is implemented, for example, by the CPU 72 repeatedly executing thedeterioration detection program 74 a stored in the ROM 74 illustrated inFIG. 1 at predetermined time intervals. For the sake of convenience, inthe process illustrated in FIG. 12, the same step numbers are assignedto the steps corresponding to the steps illustrated in FIG. 3.

In a series of the steps illustrated in FIG. 12, first, the CPU 72determines whether or not the logical sum of a condition (D) thatindicates a return from the fuel cutoff process and a condition (E)indicating that the oxygen storage amount is zero is true (S82). In thisstep, it is determined whether or not the oxygen storage amount of theupstream catalyst 32 is zero or the maximum value Cmax. Whether theoxygen storage amount is zero may be detected, for example, when thefeedback control of the air-fuel ratio is greatly deviated and thedownstream detection value Afd is set to be rich shortly after theupstream detection value Afu is set to be rich.

When it is determined that the logical sum is true (S82: YES), the CPU72 executes steps S10 to S24. When the step S14 is executed once, theCPU 72 does not calculate the deterioration level variable Rd until itis determined again in step S82 that the logical sum is true.

As described above, according to the present embodiment, the initialvalue of the time series data that is input to the mapping can be fixedwhen the oxygen storage amount of the upstream catalyst 32 correspondsto the maximum value Cmax or zero. For this reason, the mapping thatoutputs the deterioration level variable Rd may be a mapping that usestime series data corresponding to when the oxygen storage amount of theupstream catalyst 32 corresponds to the maximum value Cmax or zero as aninput and outputs the deterioration level variable Rd. The structure ofthe mapping may be simplified as compared to a mapping that outputs thedeterioration level variable Rd in any situation.

Eighth Embodiment

Hereinafter, an eighth embodiment will be described with reference tothe drawings focusing on the differences from the first embodiment.

In the present embodiment, the process that calculates the deteriorationlevel variable Rd is performed outside the vehicle.

FIG. 13 illustrates a catalyst deterioration detection system accordingto the present embodiment. For the sake of convenience, in FIG. 13, thesame reference characters are given to the members corresponding to themembers illustrated in FIG. 1.

The control device 70 installed in the vehicle VC illustrated in FIG. 13includes a communication device 79. The communication device 79 is adevice for communicating with a center 120 via a network 110 outside thevehicle VC.

The center 120 analyzes data received from a plurality of vehicles VC.The center 120 includes a CPU 122, a ROM 124, a storage device 126,peripheral circuitry 127, and a communication device 129, and thesedevices are configured to communicate with each other through a localnetwork 128. The ROM 124 stores a deterioration detection main program124 a. The storage device 126 stores a map data 126 a.

The center 120 is configured to communicate with a terminal 130 of aused vehicle dealer through the network 110.

FIG. 14 shows the sequence of a process executed by the systemillustrated in FIG. 13. The process illustrated in (a) in FIG. 14 isimplemented by the CPU 72 executing a deterioration detectionsub-program 74 c stored in the ROM 74 illustrated in FIG. 13. Theprocess illustrated in (b) in FIG. 14 is implemented by the CPU 122executing the deterioration detection main program 124 a and a stateinformation provision program 124 b that are stored in the ROM 124. Forthe sake of convenience, in FIG. 14, the same step numbers are given tothe steps corresponding to the steps illustrated in FIG. 2. Hereinafter,the processes illustrated in FIG. 14 will be described along the timeseries of a deterioration detection process.

As illustrated in (a) in FIG. 14, the CPU 72 installed in the vehicle VCacquires variables as the input to the mapping in addition to the timeseries data acquired in step S10 (S90). More specifically, the CPU 72acquires time series data of an increase amount average value Qiave,which is the average value of increase amounts Qi of the requestinjection amount Qd from the base injection amount Qb. The increaseamount Qi may have a negative value. The increase amount Qi indicates anexcess or deficiency amount of the actual fuel in relation to the amountof fuel that sets the air-fuel ratio of the mixture to thestoichiometric air-fuel ratio. The increase amount Qi configures theexcess amount variable.

The CPU 72 acquires the following variables as variables that arerelated to the operating amounts of the operation units of the internalcombustion engine 10. The variables change the combustion of the mixturein the combustion chamber 20 to change the compositions of the fluidflowing into the upstream catalyst 32. More specifically, the CPU 72acquires time series data of an ignition timing average value aigave,which is an average value of the ignition timings aig that are set bythe ignition device 24. In addition, the CPU 72 acquires time seriesdata of an EGR rate average value Regrave, which is an average value ofEGR rates Regr, that is, the ratio of the flow rate of the fluid to thesum of the flow rate of air taken into the intake passage 12 and theflow rate of the same fluid flowing from the EGR passage 36 into theintake passage 12. In addition, the CPU 72 acquires time series data ofan overlap average value ROave, which is an average value of overlapsRO, that is, a period in which the valve opening period of the intakevalve 18 overlaps the valve opening period of the exhaust valve 28. TheCPU 72 acquires time series data of an injection division ratio averagevalue Kpave, which is an average value of the injection division ratiosKp.

In addition, the CPU 72 acquires an alcohol concentration Dal in thefuel. This variable is acquired taking into consideration that thestoichiometric air-fuel ratio of the fuel changes in accordance withalcohol concentration. The alcohol concentration Dal may be estimatedfrom, for example, the correction ratio 5 of the feedback process M12described above. The CPU 72 acquires the atmospheric pressure Pa and theambient temperature TO as variables that are related to the environmentsand change the combustion of the mixture in the combustion chamber 20 tochange the compositions of the fluid flowing into the upstream catalyst32.

The CPU 72 acquires a sulfur deposition amount Qs of the upstreamcatalyst 32, which is one of the state variables of the upstreamcatalyst 32 that is related to changing over time. This variable isacquired taking into consideration that the purification capacity of theupstream catalyst 32 changes in accordance with the sulfur depositionamount Qs. The CPU 72 calculates the sulfur deposition amount Qs througha process that integrates a value obtained by multiplying the requestinjection amount Qd by a predetermined factor. The CPU 72 acquires themaximum value Cmax at the reference temperature, a length Lud from anupstream side to a downstream side, and a support amount Qpm of a noblemetal as specification variables, that is, the state variables of theupstream catalyst 32 indicating the specifications. This is a settingfor calculating the deterioration level of the upstream catalyst 32having various specifications using single map data.

When step S90 is executed, the CPU 72 operates the communication device79 to transmit the data acquired in step S90 to the center 120, togetherwith a vehicle ID, that is, identification information of the vehicle VC(S92).

As illustrated in (b) in FIG. 14, the CPU 122 of the center 120 receivesthe transmitted data (S96), and assigns the data acquired by step S90 tothe input variable x of the mapping (S12 d). Here, in addition to stepS12, when m=1 to sn, the CPU 122 assigns an increase amount averagevalue Qiave (m) to an input variable x (6sn+m), assigns an ignitiontiming average value aigave (m) to an input variable x (7sn+m), andassigns an EGR rate average value Regrave (m) to an input variable x(8sn+m). In addition, the CPU 122 assigns an overlap average value ROave(m) to an input variable x (9sn+m), and assigns an injection divisionratio average value Kpave (m) to an input variable x (10sn+m). The CPU122 assigns the alcohol concentration Dal to an input variable x (11sn+1), assigns the atmospheric pressure Pa to an input variable x(11sn+2), assigns the ambient temperature TO to an input variable x(111sn+3), and assigns the sulfur deposition amount Qs to an inputvariable x (11 sn+4). The CPU 122 assigns the maximum value Cmax to aninput variable x (11sn+5), assigns the length Lud to an input variable x(11 sn+6), and assigns the support amount Qpm to an input variable x (11sn+7).

Then, the CPU 122 calculates the deterioration level variable Rd byinputting the input variables x (1) to x (11sn+7) generated by S12 d toa mapping that is specified by the map data 126 a (S14 d). A factor wjiof the input of the mapping that is specified by the map data 126 a iswhere “i=0 to 11sn+7”.

Then, the CPU 122 operates the communication device 129 to transmit asignal related to the deterioration level variable Rd to the vehicle VCfrom which the data is received in step S96 (S98). As illustrated in (a)in FIG. 14, the CPU 72 receives the deterioration level variable Rd(S94), and executes steps S16 to S24.

As illustrated in (b) in FIG. 14, the CPU 122 updates the deteriorationlevel variable Rd related to the vehicle specified by the vehicle ID(S100) in state information data 126 b that is stored in the storagedevice 126 illustrated in FIG. 13.

The CPU 122 determines whether or not the terminal 130 of the usedvehicle dealer sends a request for vehicle state information such as thedeterioration level variable Rd related to a specified vehicle (S102).When it is determined that the request is received (S102: YES), the CPU122 accesses the storage device 126 to search for the deteriorationlevel variable Rd corresponding to the vehicle ID (S104). Then, the CPU122 operates the communication device 129 to output, for example, thedeterioration level variable Rd corresponding to the requested vehicleID to the terminal 130 as state information of the used vehicle (S106).

When step S106 is completed or when it is determined in step S102 thatthe request is not received, the CPU 122 temporarily ends the processillustrated in (b) in FIG. 14. Steps S96 to S98 are specified in thedeterioration detection main program 124 a. Steps S100 to S106 arespecified in the state information provision program 124 b.

As described above, in the present embodiment, since the deteriorationlevel variable Rd is calculated in the center 120, the computationalload of the CPU 72 is reduced.

In addition, in the present embodiment, the deterioration level variableRd is stored and updated together with the vehicle ID in the center 120.Thus, when a vehicle specified by the vehicle ID is sent to the usedvehicle dealer and the used vehicle dealer sells the vehicle as a usedvehicle, the used vehicle dealer is provided with the deteriorationlevel variable Rd as state information indicating the state of the usedvehicle. Therefore, a customer who considers purchasing the used vehicleis able to obtain detailed information regarding the deterioration levelof the vehicle that the customer considers purchasing.

Correspondence Relationship

The correspondence relationships between the items in theabove-described embodiments and the items described in the sectiontitled “Summary” are as follows. Hereinafter, the correspondencerelationship is shown with the numeral of each aspect described in thesection titled “Summary.”

[1 to 4] The execution device, namely, the processing circuitry,corresponds to the CPU 72 and the ROM 74. The first predetermined periodand the second predetermined period correspond to the periods in whichthe upstream average values Afuave (1) to Afuave (sn) are sampled. Theacquisition process corresponds to steps S10 and S10 c. Thedeterioration level variable calculation process corresponds to stepsS12 and S14; steps S12 a, S14 a, and S52; steps S12 b and S14 b; andsteps S12 c and S14 c. The dealing process corresponds to step S18 andthe process illustrated in FIG. 4. The time series data related to theexcess amount variable corresponds to the time series data of each ofthe upstream average value Afuave, the rotational speed NE, and thecharging efficiency q in the process illustrated in FIG. 3. Thedownstream detection variable corresponds to the time series data of thedownstream average value Afdave.

[5] The selection process corresponds to step S62 and steps S70 to S78.

[6 and 7] The process corresponds to step S62.

[8] The process corresponds to the process illustrated in FIG. 10.

[9] The predetermined condition corresponds to the conditions (A) and(B) in step S80.

[10] The predetermined condition corresponds to the condition (C) instep S80.

[11] The predetermined condition corresponds to a condition that isdetermined in step S82 whether or not the condition is satisfied.

[12] The limiting process corresponds to steps S34 and S36. Morespecifically, the limiting process corresponds to, for example, aprocess that operates the inverters 58 and 60, which are predeterminedhardware, to increase the output of the motor-generators 52 and 54 sothat the internal combustion engine 10 is set to the stop state.

[13] The catalyst deterioration detection system corresponds to thecontrol device 70 and the center 120. The value corresponding to theoxygen storage amount corresponds to the deterioration level variableRd. The first execution device corresponds to the CPU 72 and the ROM 74.The second execution device corresponds to the CPU 122 and the ROM 124.The acquisition process corresponds to step S90. The vehicle sidetransmission process corresponds to step S92. The vehicle side receptionprocess corresponds to step S94. The outside reception processcorresponds to step S96. The oxygen storage amount calculation processcorresponds to steps S12 d and S14 d. The vehicle side transmissionprocess corresponds to step S98.

[14] The data analysis device corresponds to the center 120.

[15] The control device of the internal combustion engine corresponds tothe control device 70.

[16] The acquisition process corresponds to step S90. The deteriorationlevel variable calculation process corresponds to steps S12 d and S14 d.The storage process corresponds to step S100. The output processcorresponds to step S106. The computer corresponds to the CPUs 72 and122 and ROMs 74 and 124.

Other Embodiments

These embodiments can be implemented with the following modifications.The combinations of these embodiments and the following modificationexamples can be made and implemented without departing from thetechnical scope.

First Predetermined Period and Second Predetermined Period

In the above-described configuration, the first predetermined period,which is the sampling period of the time series data of the upstreamaverage value Afuave, and the second predetermined period, which is thesampling period of the time series data of the downstream average valueAfdave, are set to the same period. However, the first predeterminedperiod and the second predetermined period are not limited to being thesame period. For example, the second predetermined period may be aperiod that is slightly delayed in relation to the first predeterminedperiod. In this case, when the flow rate of the fluid flowing into theupstream catalyst 32 is large, the delay time of the secondpredetermined period in relation to the first predetermined period maybe shortened as compared to when the flow rate of the fluid is small.However, the delay time may be a fixed value to reduce the number ofman-hours for performing the adaptation.

In addition, the length of the first predetermined period does notnecessarily have to be the same as the length of the secondpredetermined period.

Time Series Data of Excess Amount Variable

The time series data of the excess amount variable is not limited tothose provided as examples in the above-described embodiments. Morespecifically, the time series data of the excess amount variable is notlimited to a data set of the three state variables, that is, theupstream average value Afuave, the rotational speed NE, and the chargingefficiency η where the number of samplings is the same, or a data set oftime series data of the upstream average value Afuave, the rotationalspeed NE, and the charging efficiency η where the number of samplings ofthe rotational speed NE and the charging efficiency η is less than thatof the upstream average value Afuave. For example, in the cases of theabove-described embodiments, without including the rotational speed NEand the charging efficiency η in the input to the mapping, thein-catalyst flow rate CF may be regarded as a variable that determinesthe flow rate of the fluid flowing into the upstream catalyst 32.Alternatively, for example, the intake air amount Ga may be used as avariable that determines the flow rate of the fluid flowing into theupstream catalyst 32 and is included in the time series data of theexcess amount variable.

For example, the time series data of the excess amount variable may beconfigured by the time series data of the upstream average value Afuavein the predetermined period and a single piece of data of a variable inthe predetermined period that determines the flow rate of the fluidflowing into the upstream catalyst 32. More specifically, for example,the time series data of the excess amount variable may be the timeseries data of the upstream average value Afuave in the predeterminedperiod and a single set of the rotational speed NE and the chargingefficiency η in the predetermined period. In this case, the rotationalspeed NE and the charging efficiency η are variables that determine theflow rate of the fluid flowing into the upstream catalyst 32 in thepredetermined period corresponding to the time series data of theupstream average value Afuave. It is assumed that a change in the flowrate in the predetermined period is negligible.

The time series data related to the excess amount variable is notlimited to the time series data of the upstream average value Afuave.For example, when shortening the sampling time interval of the upstreamdetection value Afu, the time series data of the upstream detectionvalue Afu may be used as the time series data related to the excessamount variable.

Furthermore, the excess amount variable is not limited to the datarelated to the upstream detection value Afu. For example, the integratedvalue of the request injection amount Qd in the time series datasampling interval and the time series data of the intake air amount Gamay be used. In addition, for example, the increase amount Qi and therotational speed NE may be used.

Downstream Detection Variable

The input to the mapping may be the time series data of the downstreamdetection value Afd instead of the time series data of the downstreamaverage value Afdave.

In-Catalyst Flow Rate CF

The input to the mapping may be a single sampling value of thein-catalyst flow rate CF instead of the time series data of thein-catalyst flow rate CF.

In the above-described embodiments, the in-catalyst flow rate CF iscalculated from the rotational speed NE and the charging efficiency η.However, the in-catalyst flow rate CF is not limited to being calculatedtherefrom. For example, a pressure sensor and a temperature sensor maybe provided upstream of the upstream catalyst 32 in the exhaust passage30 in the proximity of the upstream catalyst 32, and the in-catalystflow rate CF may be calculated based on values from these sensors andthe intake air amount Ga.

Catalyst Temperature

The input to the mapping may be a single sampling value of the catalysttemperature Tcat instead of the time series data of the catalysttemperature Tcat or the time series data of the catalyst temperatureaverage value Tcatave.

In addition, for example, the catalyst temperature Tcat may be omittedfrom the input to the mapping. Instead, all the inputs of the catalysttemperature calculation process M22 may be input to the same mapping.With this configuration, the deterioration level variable Rd is alsocalculated with high accuracy, for example, by increasing the number ofthe intermediate layers.

Input to Mapping

In the input to the mapping of the above-described embodiment, the setof the rotational speed NE and the charging efficiency η is used as theoperating point variables that specify the operating points of theinternal combustion engine 10. Instead, for example, the intake airamount Ga and the rotational speed NE may be used as the operating pointvariables. In addition, for example, as described below in the sectiontitled “Internal Combustion Engine,” in a case in which the presentdisclosure is applied to a compression ignition internal combustionengine, the rotational speed NE and the injection amount or therotational speed NE and the accelerator pedal operating amount ACCP maybe used as the operating point variables. However, the operating pointvariables do not necessarily have to be included in the input to themapping.

In the input to the mapping provided as an example in the processillustrated in FIG. 14, step S14 d does not necessarily have to beexecuted in the center 120. In other words, steps S12 d and S14 d may beexecuted in the control device 70.

The input variables provided as examples in step S14 d may be changed,for example, as follows.

For example, instead of using the time series data of the increaseamount average value Qiave, the time series data of the increase amountQi may be used. The input variable is not limited to the increase amountQi or the increase amount average value Qiave. For example, the timeseries data of an excess rate that is obtained by dividing the increaseamount Qi by the base injection amount Qb, the time series data of theaverage value of the excess rates, or the time series data of therequest injection amount Qd or the average value of the requestinjection amounts Qd may be used. In this case, the number of samplingsof the time series data that is input to the mapping does notnecessarily have to be the same as the number of samplings of thedownstream average value Afdave. Instead of using the time series data,a single sampling value of the increase amount Qi, the excess rate, orthe request injection amount Qd in the predetermined period may be used.Instead of using the time series data, a single sampling value of theincrease amount average value Qiave, the average value of the excessrates, or the average value of the request injection amounts Qd in thepredetermined period may be used. In this case, the average value may bean average value over the predetermined period. The above-describedvariables related to the injection amount do not necessarily have to beincluded in the input to the mapping in processes such as the processillustrated in (b) in FIG. 14.

In addition, for example, instead of using the time series data of theignition timing average value aigave, the time series data of theignition timing aig may be used. In addition, the ignition timingaverage value aigave and the ignition timing aig are not limited to thetime series data. A single sampling value of the ignition timing aig ora single sampling value of the ignition timing average value aigave inthe predetermined period may be used. In this case, the ignition timingaverage value aigave may be an average value obtained over thepredetermined period. These variables related to the ignition timing aigdo not necessarily have to be included in the input to the mapping inprocesses such as the process illustrated in (b) in FIG. 14.

In addition, for example, instead of using the time series data of theEGR rate average value Regrave, the time series data of the EGR rateRegr itself may be used. In addition, the EGR rate Regr and the EGR rateaverage value Regrave are not limited to the time series data. A singlesampling value of the EGR rate Regr or a single sampling value of theEGR rate average value Regrave in the predetermined period may be used.In this case, the EGR rate average value Regrave may be an average valueobtained over the predetermined period. These variables related to theEGR rate Regr do not necessarily have to be included in the input to themapping in processes such as the process illustrated in (b) in FIG. 14.

In addition, for example, instead of using the time series data of theoverlap average value ROave, the time series data of the overlap RO maybe used. The overlap average value ROave and the overlap RO are notlimited to the time series data. A single sampling value of the overlapRO or a single sampling value of the overlap average value ROave in thepredetermined period may be used. In this case, the overlap averagevalue ROave may be an average value obtained over the predeterminedperiod. A data set of the valve opening timing of the intake valve 18and the valve opening timing of the exhaust valve 28 may be used as avariable related to the overlap. These variables related to the overlapRO do not necessarily have to be included in the input to the mapping inprocesses such as the process illustrated in (b) in FIG. 14.

In addition, for example, instead of the time series data of theinjection division ratio average value Kpave, the time series data ofthe injection division ratio Kp itself may be used. In addition, theinjection division ratio average value Kpave and the injection divisionratio Kp are not limited to the time series data, and a single samplingvalue of the injection division ratio Kp or a single sampling value ofthe injection division ratio average value Kpave in the predeterminedperiod may be used. In this case, the injection division ratio averagevalue Kpave may be an average value over the predetermined period. Thevariables related to the injection division ratio Kp do not necessarilyhave to be included in the input to the mapping in processes such as theprocess illustrated in (b) in FIG. 14.

In addition, for example, instead of using the alcohol concentrationDal, the average value of the alcohol concentrations Dal in thepredetermined period may be used. The time series data of the alcoholconcentration Dal or the time series data of the average value of thealcohol concentrations Dal in the predetermined period may be used. Afuel property variable, that is, a variable indicating the properties ofthe fuel, is not limited to a stoichiometric air-fuel ratio variableindicating the difference of the stoichiometric air-fuel ratio of thefuel such as the alcohol concentration Dal. The fuel property variablemay be, for example, a variable indicating whether the fuel is a heavyfuel or a light fuel. The fuel property variable does not necessarilyhave to be included in the input to the mapping in processes such as theprocess illustrated in (b) in FIG. 14.

In addition, for example, instead of using the atmospheric pressure Pa,the average value of the atmospheric pressure Pa in the predeterminedperiod may be used. The time series data of the atmospheric pressure Paor the time series data of the average value of the atmospheric pressurePa in the predetermined period may be used. The variables related to theatmospheric pressure Pa do not necessarily have to be included in theinput to the mapping in processes such as the process illustrated in (b)in FIG. 14.

In addition, for example, instead of using the ambient temperature TO,the average value of the ambient temperatures TO in the predeterminedperiod may be used. The time series data of the ambient temperature TOor the time series data of the average value of the ambient temperaturesTO in the predetermined period may be used. The variables related to theambient temperature TO do not necessarily have to be included in theinput to the mapping in processes such as the process illustrated in (b)in FIG. 14.

In addition, for example, instead of using the sulfur deposition amountQs, the average value of the sulfur deposition amounts Qs in thepredetermined period may be used. The time series data of the sulfurdeposition amount Qs or the time series data of the average value of thesulfur deposition amounts Qs in the predetermined period may be used.The variables related to the sulfur deposition amount Qs do notnecessarily have to be included in the input to the mapping in processessuch as the processes illustrated in (b) in FIG. 14.

The specification variables determining the specifications of theupstream catalyst 32 are not limited to the three variables, namely, themaximum value Cmax, the length Lud from upstream to downstream, and thesupport amount Qpm. For example, only one or two of the three parametersmay be used. The specification variables do not necessarily have to beincluded in the input to the mapping in processes such as the processillustrated in (b) in FIG. 14.

For example, as described below in the section titled “InternalCombustion Engine,” in a case in which the internal combustion engine 10includes a turbocharger and a wastegate valve, the opening degree of thewastegate valve may be included in the input to the mapping. Morespecifically, the flow of the fluid to the upstream catalyst 32 changesin accordance with the opening degree of the wastegate valve and thusaffects consumption of the stored oxygen. Such a situation is learned,when the opening degree is included in the input to the mapping.

The cases in which the variables related to the flow rate of the fluidflowing into the upstream catalyst 32 are not input to the mapping arenot limited to those provided as examples in the above-describedembodiments. For example, in a case in which the internal combustionengine is installed in a series hybrid vehicle such as that describedbelow in the section titled “Vehicle” is driven only at predeterminedoperating points, the variables related to the flow rate of the fluidflowing into the upstream catalyst 32 do not have to be input to themapping. For example, as described below in the section titled “SamplingPeriod of Time Series Data,” in a case in which the process that changesthe target value Af* for the calculation of the deterioration levelvariable Rd is executed only at predetermined operating points, thevariables related to the flow rate of the fluid flowing into theupstream catalyst 32 do not have to be input to the mapping.

The input to the neural network and the input to the regressionequation, which are described below in the section titled “Algorithm ofMachine Learning,” are not limited to being formed of physicalquantities each having a single dimension. For example, in theabove-described embodiments, different kinds of the physical quantitiesthat are input to the mapping and are directly input to the neuralnetwork or the regression equation. Instead, one or more of thedifferent kinds of the physical quantities may be analyzed for theirmain components, and the main components may be directly input to theneural network or the regression equation. However, in a case in whichmain components are input to the neural network or the regressionequation, the main components do not necessarily have to be only aportion of the input to the neural network or the regression equation.The entirety of the input may be the main components. In a case in whichthe main components are input to the mapping, the map data 76 a and 126a include data that specifies a mapping that determines the maincomponents.

Map Data

For example, the illustrations in FIG. 11 indicate that the number ofthe intermediate layers of the neural network is greater than two.However, the number of intermediate layers is not limited to beinggreater than two. In particular, from the viewpoint of reducing thecomputational load of the control device 70, the number of intermediatelayers of the neural network may be reduced to one or two. Such aconfiguration is readily implemented, for example, when the processesillustrated in FIGS. 9 to 12 are executed, as compared to when theprocess illustrated in FIG. 14 is executed.

In the above-described embodiments, the activation functions h1, h2, . .. , and ha are hyperbolic tangents and the activation function f is aReLU. However, the present disclosure is not limited to such aconfiguration. For example, each of the activation functions h1, h2, . .. , and hα may be a ReLU. For example, the activation functions h1, h2,. . . , and hα may be logistic sigmoid functions. For example, theactivation function f may be a logistic sigmoid function.

Different Kinds of Map Data

In the process illustrated in FIG. 9, the rotational speed NE and thecharging efficiency η are used as the variables related to the flow rateof the fluid flowing into the upstream catalyst 32, and different piecesof map data are used for each of the areas divided by the rotationalspeed NE and the charging efficiency η. However, the variables relatedto the flow rate of the fluid flowing into the upstream catalyst 32 arenot limited thereto. For example, the intake air amount Ga or thein-catalyst flow rate CF may be used.

In the process illustrated in FIG. 9, different pieces of map data areused for each of the areas divided by the variables related to the flowrate of the fluid flowing into the upstream catalyst 32 and the catalysttemperature Tcat. However, the present disclosure is not limited to theconfiguration described above. For example, regardless of the catalysttemperature Tcat, different pieces of map data may be used for each ofareas that are divided by the variables related to the flow rate of thefluid flowing into the upstream catalyst 32. In addition, for example,regardless of the variables related to the flow rate of the fluidflowing into the upstream catalyst 32, different pieces of map data maybe used for each of areas that are divided by the catalyst temperatureTcat.

In the process illustrated in FIG. 10, when a negative determination ismade in S72, different pieces of map data may be used for each of areasthat are divided by the variables related to the flow rate of the fluidflowing into the upstream catalyst 32. Alternatively, for example, whena negative determination is made in S72, different pieces of map datamay be used for each of areas that are divided by the catalysttemperature Tcat. For example, when a negative determination is made inS72, different pieces of map data may be used for each of areas that aredivided by the variables related to the flow rate of the fluid flowinginto the upstream catalyst 32 and the catalyst temperature Tcat.

The input of the map data corresponding to a case in which differentkinds of map data are provided is not limited to those provided asexamples in the above-described embodiments. For example, regardless ofthe variables related to the flow rate of the fluid flowing into theupstream catalyst 32, when different pieces of map data are used foreach of the areas that are divided by the catalyst temperature Tcat, theinput to the mapping may include the variables related to the flow rateof the fluid flowing into the upstream catalyst 32. In addition, forexample, regardless of the catalyst temperature Tcat, when differentpieces of map data are used for each of areas that are divided by thevariables related to the flow rate of the fluid flowing into theupstream catalyst 32, the input to the mapping may include the catalysttemperature Tcat. The configuration that does not include the variablesused to divide the areas in the input to the mapping is not necessary.For example, in a case in which different pieces of map data are usedfor each of areas that are divided by the variables related to the flowrate of the fluid flowing into the upstream catalyst 32 and the catalysttemperature Tcat, the input to the mapping may include the variablesrelated to the flow rate of the fluid flowing to the upstream catalyst32 and the catalyst temperature Tcat. Furthermore, the input to themapping may also include, for example, variables that do not directlydetermine the division of an area such as the increase amount averagevalue Qiave.

Predetermined Conditions

In step S80 illustrated in FIG. 11, the condition indicating that theflow rate of the fluid flowing into the upstream catalyst 32 is within apredetermined range is a condition indicating that the logical productof the condition (A) and the condition (B) is true. However, the presentdisclosure is not limited to such a configuration. For example, acondition indicating that the intake air amount Ga or the in-catalystflow rate CF is within a predetermined range may be used.

In step S80 illustrated in FIG. 11, the predetermined condition for thesampling of the variables used in calculating the deterioration levelvariable Rd is a condition indicating that the logical product of thecondition indicating that the flow rate of the fluid flowing into theupstream catalyst 32 is within the predetermined range and the condition(C) is true. However, the present disclosure is not limited to such aconfiguration. A condition indicating that only either one of the twoconditions is satisfied may be used.

The input of the map data in the process illustrated in FIG. 11 and itsmodification examples is not limited to those provided as examples inthe above-described embodiments. For example, regardless of thevariables related to the flow rate of the fluid flowing into theupstream catalyst 32, when the catalyst temperature Tcat is within thepredetermined range, in a case in which the sampling of the variablesused in calculating the deterioration level variable Rd is allowed, theinput to the mapping may include the variables related to the flow rateof the fluid flowing into the upstream catalyst 32. In addition, forexample, regardless of the catalyst temperature Tcat, when the flow rateof the fluid flowing into the upstream catalyst 32 is within thepredetermined range, in a case in which the sampling of the variablesused in calculating the deterioration level variable Rd is allowed, theinput to the mapping may include the catalyst temperature Tcat. Theconfiguration that does not include the variables that are included inthe condition that allows the sampling of the variables used incalculating the deterioration level variable Rd in the input to themapping. For example, when the logical product of the above-describedconditions (A) to (C) is true, in a case in which the sampling of thevariables which are used in calculating the deterioration level variableRd is allowed, the input to the mapping includes the variables relatedto the flow rate of the fluid flowing into the upstream catalyst 32 andthe catalyst temperature Tcat. Furthermore, the input to the mapping mayalso include, for example, variables that do not directly determine thecondition that allows the sampling of the variables used to calculatethe deterioration level variable Rd, such as the increase amount averagevalue Qiave.

In step S82 illustrated in FIG. 12, the predetermined condition for thesampling of the variables which is used to calculate the deteriorationlevel variable Rd is a condition indicating that the logical sum of thecondition (D) and the condition (E) is true. However, the presentdisclosure is not limited to such a configuration. For example, acondition indicating that the condition (D) is satisfied may be used.

Sampling Period of Time Series Data

In the above-described embodiments, when the target value Af* is set asdescribed above, time series data that is input to the mapping issampled. However, the present disclosure is not limited to such aconfiguration. For example, the target value Af* may be set for thecalculation of the deterioration level variable Rd. Also, in this case,when the time series data provided as examples in the above-describedembodiments are used, the target value Af* may be set to further reducethe amount of deviation of the composition of the fluid flowing into thecatalyst from the composition that is appropriate for the purificationperformance of the catalyst and to further shorten a period in which theamount of deviation increases, as compared to a case in which thetechnique in the related art is used.

Dealing Process

The notification process is not limited to operating a device thatoutputs visual information such as the warning lamp 99. The notificationprocess may be, for example, a process that operates a device thatoutputs voice information.

The dealing process is not limited to performing all of steps S34, S36,and S38. Among the three steps, only one step may be performed.Alternatively, for example, only two of the three steps may beperformed. Without executing the process illustrated in FIG. 4, onlystep S18 may be executed.

In addition, for example, when heating control such as a sulfurdeposition removal process is performed on the upstream catalyst 32, theoperating amount of the heating control may be changed in accordancewith the deterioration level of the upstream catalyst 32. In this case,in a case in which the same operating amount is set for when thedeterioration level is high and when the deterioration level is low,since the temperature rise speed is lower when the deterioration levelis high, the operating amount basically may be changed to increase thetemperature rise speed.

Machine Learning Algorithm

An algorithm of machine learning is not limited to a neural network. Forexample, a regression equation may be used. This is equivalent to a casein which intermediate layers are not provided in the above-describedneural network.

Map Data Generation

In the above-described embodiments, the data acquired when the internalcombustion engine 10 operates in a state where the dynamometer 100 isconnected to the crankshaft 26 is used as training data. However, thepresent disclosure is not limited to such a configuration. For example,data that is acquired when the internal combustion engine 10 is drivenin a state where the internal combustion engine 10 is installed in thevehicle VC may be used as training data.

Data Analysis Device

Instead of steps S12 d and S14 d, the center 120 may execute the processthat calculates the maximum value Cmax, and may transmit the maximumvalue Cmax to the vehicle VC as in steps S12 a and S14 a.

The center 120 may execute steps S16 and S18, and may execute, as stepS18, a process that notifies a mobile terminal of a user that there isan abnormality.

Steps S96, S12 d, S14 d, and S98 illustrated in (b) in FIG. 14 may beexecuted, for example, by the mobile terminal held by the user.

Execution Device

The execution device is not limited to including the CPU 72 (CPU 122)and the ROM 74 (ROM 124), and executing software processes by using theCPU 72 (CPU 122) and the ROM 74 (ROM 124). For example, the executiondevice may include a dedicated hardware circuit (for example, ΔδIC) thatprocesses at least some of the software processes executed in theabove-described embodiments. More specifically, the execution device mayhave any one of the following configurations (a) to (c). Configuration(a) includes a processing device that executes all of theabove-described processes according to a program and a program storagedevice such as a ROM that stores the program. Configuration (b) includesa processing device that executes some of the above-described processesaccording to a program, a program storage device, and a dedicatedhardware circuit that executes the remaining processes. Configuration(c) includes a dedicated hardware circuit that executes all of theabove-described processes. A plurality of software execution devices,each of which includes the processing device and the program storagedevice, may be provided. A plurality of dedicated hardware circuits maybe provided. More specifically, the above-described processes may beexecuted by processing circuitry including at least one of one or moreof software execution devices and one or more of dedicated hardwarecircuits. The program storage device, namely, a computer readable mediumincludes all useable media that can be accessed by general-purpose ordedicated computers.

Storage Device

In the above-described embodiments, the storage devices that store themap data 76 a and 126 a are configured to be separate from the storagedevices (ROM 74 and ROM 124) that store the deterioration detectionprogram 74 a and the deterioration detection main program 124 a.However, the storage devices are not limited to such a configuration.

State Information Providing Process

In the process illustrated in FIG. 14, steps S12 d and S14 d areperformed in the center 120. However, the present disclosure is notlimited to such a configuration. Steps S12 d and S14 d may be performedby the control device 70. In this case, the control device 70 may outputthe deterioration level variable Rd to the center 120, together with thevehicle ID, and the center 120 may execute the steps following S100. Inthat case, whenever the control device 70 calculates the deteriorationlevel variable Rd, the control device 70 does not necessarily have tooutput the deterioration level variable Rd to the center 120, togetherwith the vehicle ID. For example, the deterioration level variable Rdmay be registered to the center 120 when a used vehicle dealer purchasesthe vehicle VC as a used vehicle.

Internal Combustion Engine

The internal combustion engine is not limited to including both of theport injection valve 16 and the in-cylinder injection valve 22. Theinternal combustion engine may include only one of the two types of thefuel injection valves.

The internal combustion engine is not limited to a spark ignitioninternal combustion engine, and may be, for example, a compressionignition internal combustion engine that uses diesel as fuel.

Others

The vehicle is not limited to a series and parallel hybrid vehicle, andmay be, for example, a series hybrid vehicle or a parallel hybridvehicle. The vehicle is also not limited to a hybrid vehicle, and may bea vehicle that includes only the internal combustion engine as a devicethat generates propulsion power of the vehicle.

The catalyst is not limited to a three-way catalyst, and may have, forexample, a configuration where a three-way catalyst is supported on afilter that captures particulate matter.

Various changes in form and details may be made to the examples abovewithout departing from the spirit and scope of the claims and theirequivalents. The examples are for the sake of description only, and notfor purposes of limitation. Descriptions of features in each example areto be considered as being applicable to similar features or aspects inother examples. Suitable results may be achieved if sequences areperformed in a different order, and/or if components in a describedsystem, architecture, device, or circuit are combined differently,and/or replaced or supplemented by other components or theirequivalents. The scope of the disclosure is not defined by the detaileddescription, but by the claims and their equivalents. All variationswithin the scope of the claims and their equivalents are included in thedisclosure.

What is claimed is:
 1. A catalyst deterioration detection deviceconfigured to detect a deterioration of a catalyst provided in anexhaust passage of an internal combustion engine, the catalystdeterioration detection device, comprising: a storage device; andprocessing circuitry, wherein the storage device stores map data, themap data specifying a mapping that uses time series data of an excessamount variable in a first predetermined period and time series data ofa downstream detection variable in a second predetermined period asinputs to output a deterioration level variable, an amount of fuel thatreacts with oxygen contained in a fluid flowing into the catalystwithout excess or deficiency is an ideal fuel amount, and the excessamount variable is a variable that corresponds to an excess amount of anactual fuel flowing into the catalyst in relation to the ideal fuelamount, the downstream detection variable is a variable that correspondsto a detection value of an air-fuel ratio sensor provided downstream ofthe catalyst, the deterioration level variable is a variable related toa deterioration level of the catalyst, the processing circuitry isconfigured to execute an acquisition process that acquires the timeseries data of the excess amount variable in the first predeterminedperiod and the time series data of the downstream detection variable inthe second predetermined period, a deterioration level variablecalculation process that calculates the deterioration level variable ofthe catalyst based on an output of the mapping using the data acquiredby the acquisition process as an input, and a dealing process thatoperates a predetermined hardware when the deterioration level of thecatalyst is greater than or equal to a predetermined level based on acalculation result of the deterioration level variable calculationprocess in response to a situation in which the deterioration level ofthe catalyst is greater than or equal to the predetermined level, andthe map data includes data that is learned through machine learning. 2.The catalyst deterioration detection device according to claim 1,wherein the time series data in the second predetermined period includesvalues of the downstream detection variable that correspond to three ormore different points in time.
 3. The catalyst deterioration detectiondevice according to claim 1, wherein an input to the mapping includes atemperature of the catalyst, the acquisition process includes a processthat acquires the temperature of the catalyst, and the deteriorationlevel variable calculation process includes a process that calculatesthe deterioration level variable of the catalyst based on an output ofthe mapping that uses the temperature of the catalyst as an input. 4.The catalyst deterioration detection device according to claim 1,wherein the excess amount variable includes a variable that correspondsto a detection value of an air-fuel ratio sensor provided upstream ofthe catalyst.
 5. The catalyst deterioration detection device accordingto claim 1, wherein the map data is one of different kinds of map data,and the storage device includes the different kinds of map data, and thedeterioration level variable calculation process includes a selectionprocess that selects the map data from the different kinds of map data,the map data being used to calculate the deterioration level variable ofthe catalyst.
 6. The catalyst deterioration detection device accordingto claim 5, wherein the different kinds of map data include data foreach of areas that are divided based on a flow rate of the fluid flowinginto the catalyst, and the selection process includes a process thatselects the map data used to calculate the deterioration level variableof the catalyst based on the flow rate.
 7. The catalyst deteriorationdetection device according to claim 5, wherein the different kinds ofmap data include data for each of areas that are divided based on atemperature of the catalyst, and the selection process includes aprocess that selects the map data used to calculate the deteriorationlevel of the catalyst based on the temperature of the catalyst.
 8. Thecatalyst deterioration detection device according to claim 5, whereinthe different kinds of map data include different pieces of datacorresponding to whether a fuel cutoff process is being executed, andthe selection process includes a process that selects the map data inaccordance with whether or not the fuel cutoff process is beingexecuted.
 9. The catalyst deterioration detection device according toclaim 1, wherein the acquisition process includes a process thatacquires a variable that is used as an input to the mapping when apredetermined condition is satisfied, and the predetermined conditionincludes a condition indicating that a flow rate of the fluid flowinginto the catalyst is within a predetermined range.
 10. The catalystdeterioration detection device according to claim 1, wherein theacquisition process includes a process that acquires a variable that isused as an input to the mapping when a predetermined condition issatisfied, and the predetermined condition includes a conditionindicating that a temperature of the catalyst is within a predeterminedrange.
 11. The catalyst deterioration detection device according toclaim 1, wherein the acquisition process includes a process thatacquires a variable that is used as an input to the mapping insynchronization with a point in time at which a predetermined conditionis satisfied, and the predetermined condition is a condition indicatingthat an amount of oxygen stored in the catalyst corresponds to a maximumvalue or a minimum value.
 12. The catalyst deterioration detectiondevice according to claim 1, wherein the dealing process includes alimiting process that limits an amount of unburned fuel flowing into thecatalyst to a reduced amount.
 13. A catalyst deterioration detectionsystem, comprising: the processing circuitry and the storage deviceaccording to claim 1, wherein the deterioration level variablecalculation process includes an oxygen storage amount calculationprocess that uses at least a part of the map data to calculate a valuecorresponding to a maximum value of an oxygen storage amount of thecatalyst, the processing circuitry includes a first execution device anda second execution device, the first execution device is installed in avehicle and is configured to execute the acquisition process, a vehicleside transmission process that transmits data acquired by theacquisition process to an outside of the vehicle, a vehicle sidereception process that receives a signal based on a calculation resultof the oxygen storage amount calculation process, and the dealingprocess, and the second execution device is disposed outside the vehicleand is configured to execute an outside reception process that receivesdata transmitted by the vehicle side transmission process, the oxygenstorage amount calculation process, and an outside transmission processthat transmits a signal based on a calculation result of the oxygenstorage amount calculation process to the vehicle.
 14. A data analysisdevice, comprising: the second execution device and the storage deviceaccording to claim
 13. 15. A control device of an internal combustionengine, the control device, comprising: the first execution deviceaccording to claim
 13. 16. A method for providing state information of aused vehicle on which an internal combustion engine is mounted, theinternal combustion engine being provided with a catalyst provided in anexhaust passage, the method causing a computer to execute theacquisition process and the deterioration level variable calculationprocess according to claim 1, a storage process that stores acalculation result of the deterioration level variable calculationprocess together with a vehicle ID in a storage device, and an outputprocess that outputs deterioration level information of the catalystcorresponding to the vehicle ID in response to an access from outside.